roll back to 9f4c149

app.py

@@ -102,9 +102,7 @@ def init_leaderboard(dataframe):
 # model_result_path = "./src/results/models_2024-10-07-14:50:12.666068.jsonl"
 # model_result_path = "./src/results/models_2024-10-08-03:10:26.811832.jsonl"
 # model_result_path = "./src/results/models_2024-10-08-03:25:44.801310.jsonl"
-# model_result_path = "./src/results/models_2024-10-08-17:39:21.001582.jsonl"
-model_result_path = "./src/results/models_2024-10-09-05:17:38.810960.json"
-# model_result_path = "./src/results/models_2024-10-09-06:22:21.122422.json"
+model_result_path = "./src/results/models_2024-10-08-17:39:21.001582.jsonl"
 # model_leaderboard_df = get_model_leaderboard_df(model_result_path)
 
 
@@ -138,7 +136,7 @@ with demo:
         with gr.TabItem("🏅 Overview", elem_id="llm-benchmark-tab-table", id=0):
 
             DESCRIPTION_TEXT = """
-            Total #models: 53 (Last updated: 2024-10-08)
+            Total #models: 52 (Last updated: 2024-10-08)
             
             This page provids a comprehensive overview of model ranks across various dimensions. Models are sorted based on their averaged rank across all dimensions. 
             (Some missing values are due to the slow or problemtic model responses, and we will update the leaderboard once we have the complete results.)
@@ -157,7 +155,6 @@ with demo:
                         AutoEvalColumn.rank_math_probability.name,
                         AutoEvalColumn.rank_reason_logical.name,
                         AutoEvalColumn.rank_reason_social.name,
-                        AutoEvalColumn.rank_chemistry.name,
                         ],
                     rank_col=[],
                 )
@@ -275,7 +272,6 @@ with demo:
             [SocialIQA](https://arxiv.org/abs/1904.09728),
             [NormBank](https://arxiv.org/abs/2305.17008), covering challenging social reasoning tasks, 
             such as social commonsense reasoning, social normative reasoning, Theory of Mind (ToM) reasoning, etc.
-            More fine-grained types of reasoning, such as symbolic, analogical, counterfactual reasoning, are planned to be added in the future.
             
             """
             gr.Markdown(DESCRIPTION_TEXT, elem_classes="markdown-text")
@@ -316,10 +312,9 @@ with demo:
 
         with gr.TabItem("🔬 Science", elem_id="science-table", id=4):
             CURRENT_TEXT = """
-            Scientific tasks are crucial for evaluating LLMs, requiring both domain-specific knowledge and reasoning capabilities.
-            
-            We are adding several fine-grained scientific domains to the leaderboard. The forthcoming ones are biology, chemistry, and physics. 
-            We have diversely and aggressively collected recent scientific datasets, including but not limited to
+            # Coming soon!
+            We are working on adding more tasks on scientific domains to the leaderboard. The forthcoming ones are biology, chemistry, and physics. 
+            We have diversely and aggressively collected recent science datasets, including but not limited to
             [GPQA](https://arxiv.org/abs/2311.12022),
             [JEEBench](https://aclanthology.org/2023.emnlp-main.468/),
             [MMLU-Pro](https://arxiv.org/abs/2406.01574),
@@ -328,41 +323,12 @@ with demo:
             [SciEval](https://arxiv.org/abs/2308.13149).
             """
             gr.Markdown(CURRENT_TEXT, elem_classes="markdown-text")
-            
-            with gr.TabItem("🧪 Chemistry", elem_id="chemistry_subtab", id=0, elem_classes="subtab"):         
-                leaderboard = overall_leaderboard(
-                    get_model_leaderboard_df(
-                        model_result_path,
-                        benchmark_cols=[
-                            AutoEvalColumn.rank_chemistry.name,
-                            AutoEvalColumn.model.name, 
-                            AutoEvalColumn.score_chemistry.name,
-                            # AutoEvalColumn.sd_reason_social.name,
-                            AutoEvalColumn.license.name,
-                            AutoEvalColumn.organization.name,
-                            AutoEvalColumn.knowledge_cutoff.name,
-                            ],
-                        rank_col=[AutoEvalColumn.rank_chemistry.name],
-                    )
-                )
-
-            with gr.TabItem("⚛️ Physics", elem_id="physics_subtab", id=1, elem_classes="subtab"):   
-                CURRENT_TEXT = """
-                # Coming soon!
-                """
-                gr.Markdown(CURRENT_TEXT, elem_classes="markdown-text")
-
-
-            with gr.TabItem("🧬 Biology", elem_id="biology_subtab", id=2, elem_classes="subtab"):   
-                CURRENT_TEXT = """
-                # Coming soon!
-                """
-                gr.Markdown(CURRENT_TEXT, elem_classes="markdown-text")
 
 
         with gr.TabItem("</> Coding", elem_id="coding-table", id=5):
             CURRENT_TEXT = """
-            We are working on adding more fine-grained tasks in coding domains to the leaderboard. 
+            # Coming soon!
+            We are working on adding more tasks in coding domains to the leaderboard. 
             The forthcoming ones focus on Python, Java, and C++, with plans to expand to more languages. 
             We collect a variety of recent coding datasets, including 
             [HumanEval](https://huggingface.co/datasets/openai/openai_humaneval), 
@@ -373,24 +339,6 @@ with demo:
             Our efforts also include synthesizing new code-related queries to ensure diversity!
             """
             gr.Markdown(CURRENT_TEXT, elem_classes="markdown-text")
-            
-            with gr.TabItem("🐍 Python", elem_id="python_subtab", id=0, elem_classes="subtab"):   
-                CURRENT_TEXT = """
-                # Coming soon!
-                """
-                gr.Markdown(CURRENT_TEXT, elem_classes="markdown-text")
-
-            with gr.TabItem("☕ Java", elem_id="java_subtab", id=1, elem_classes="subtab"):   
-                CURRENT_TEXT = """
-                # Coming soon!
-                """
-                gr.Markdown(CURRENT_TEXT, elem_classes="markdown-text")
-
-            with gr.TabItem("➕ C++", elem_id="cpp_subtab", id=2, elem_classes="subtab"):   
-                CURRENT_TEXT = """
-                # Coming soon!
-                """
-                gr.Markdown(CURRENT_TEXT, elem_classes="markdown-text")
 
 
 
@@ -406,7 +354,7 @@ with demo:
             
             ## Team members
             Yanbin Yin, [Zhen Wang](https://zhenwang9102.github.io/), [Kun Zhou](https://lancelot39.github.io/), Xiangdong Zhang,
-            [Shibo Hao](https://ber666.github.io/), [Yi Gu](https://www.yigu.page/), [Jieyuan Liu](https://www.linkedin.com/in/jieyuan-liu/), [Somanshu Singla](https://www.linkedin.com/in/somanshu-singla-105636214/), [Tianyang Liu](https://leolty.github.io/),
+            [Shibo Hao](https://ber666.github.io/), [Yi Gu](https://www.yigu.page/), Jieyuan Liu, Somanshu Singla, [Tianyang Liu](https://leolty.github.io/),
             [Eric P. Xing](https://www.cs.cmu.edu/~epxing/), [Zhengzhong Liu](https://hunterhector.github.io/), [Haojian Jin](https://www.haojianj.in/),
             [Zhiting Hu](https://zhiting.ucsd.edu/)
             

requirements.txt

@@ -2,7 +2,7 @@ APScheduler
 black
 datasets
 gradio
-# gradio[oauth]
+gradio[oauth]
 gradio_leaderboard==0.0.9
 gradio_client
 huggingface-hub>=0.18.0

src/display/utils.py

@@ -63,7 +63,7 @@ auto_eval_column_dict.append(["score", ColumnContent, field(default_factory=lamb
 auto_eval_column_dict.append(["score_sd", ColumnContent, field(default_factory=lambda: ColumnContent("Score SD", "number", True))])
 auto_eval_column_dict.append(["rank", ColumnContent, field(default_factory=lambda: ColumnContent("Rank", "number", True))])
 
-# fine-grained dimensions
+# fine-graine dimensions
 auto_eval_column_dict.append(["score_overall", ColumnContent, field(default_factory=lambda: ColumnContent("Score (Overall)", "number", True))])
 auto_eval_column_dict.append(["score_math_algebra", ColumnContent, field(default_factory=lambda: ColumnContent("Score (Math Algebra)", "number", True))])
 auto_eval_column_dict.append(["score_math_geometry", ColumnContent, field(default_factory=lambda: ColumnContent("Score (Math Geometry)", "number", True))])
@@ -85,9 +85,6 @@ auto_eval_column_dict.append(["rank_math_probability", ColumnContent, field(defa
 auto_eval_column_dict.append(["rank_reason_logical", ColumnContent, field(default_factory=lambda: ColumnContent("Rank (Logical Reasoning)", "number", True))])
 auto_eval_column_dict.append(["rank_reason_social", ColumnContent, field(default_factory=lambda: ColumnContent("Rank (Social Reasoning)", "number", True))])
                              
-auto_eval_column_dict.append(["score_chemistry", ColumnContent, field(default_factory=lambda: ColumnContent("Score (Chemistry)", "number", True))])
-auto_eval_column_dict.append(["sd_chemistry", ColumnContent, field(default_factory=lambda: ColumnContent("Std dev (Chemistry)", "number", True))])
-auto_eval_column_dict.append(["rank_chemistry", ColumnContent, field(default_factory=lambda: ColumnContent("Rank (Chemistry)", "number", True))])
 
 for task in Tasks:
     auto_eval_column_dict.append([task.name, ColumnContent, field(default_factory=lambda: ColumnContent(task.value.col_name, "number", True))])

src/leaderboard/read_evals.py

@@ -115,7 +115,7 @@ class ModelResult:
                     new_v[kk] = vv
                     
             new_results[k] = new_v
-            
+
         # Extract results available in this file (some results are split in several files)
         # results = {}
         # for domain in Domains:
@@ -185,10 +185,6 @@ class ModelResult:
             AutoEvalColumn.rank_reason_logical.name: self.results.get("Logical").get("Rank", None),
             AutoEvalColumn.rank_reason_social.name: self.results.get("Social").get("Rank", None),
             
-            AutoEvalColumn.score_chemistry.name: self.results.get("Chemistry").get("Average Score", None) if self.results.get("Chemistry") else None,
-            AutoEvalColumn.sd_chemistry.name: self.results.get("Chemistry").get("Standard Deviation", None) if self.results.get("Chemistry") else None,
-            AutoEvalColumn.rank_chemistry.name: self.results.get("Chemistry").get("Rank", None) if self.results.get("Chemistry") else None,
-            
             AutoEvalColumn.license.name: self.license,
             AutoEvalColumn.organization.name: self.org,
             AutoEvalColumn.knowledge_cutoff.name: self.knowledge_cutoff,

src/populate.py

@@ -24,17 +24,11 @@ def get_model_leaderboard_df(results_path: str, requests_path: str="", cols: lis
     if rank_col: # if there is one col in rank_col, sort by that column and remove NaN values
         df = df.dropna(subset=benchmark_cols)
         df = df.sort_values(by=[rank_col[0]], ascending=True)
-        # print(rank_col)
-    else: 
-        # when rank_col, the first in benchmark_cols is empty, sort by averaging all the benchmarks, except the first one
-        avg_rank = df.iloc[:, 1:].mean(axis=1) 
-        df["Average Rank"] = avg_rank.round(decimals=4)
-        df = df.sort_values(by=["Average Rank"], ascending=True)        
-        df["Average Rank"] = df["Average Rank"].map('{:.4f}'.format)
-
-        # we'll skip NaN, instrad of deleting the whole row
+    else: # when rank_col is empty, sort by averaging all the benchmarks, except the first one
+        avg_rank = df.iloc[:, 1:].mean(axis=1) # we'll skip NaN, instrad of deleting the whole row
+        df["Average Rank"] = avg_rank
+        df = df.sort_values(by=["Average Rank"], ascending=True)
         df = df.fillna('--')
-        # insert a rank column
         rank = np.arange(1, len(df)+1)
         df.insert(0, 'Rank', rank)
         
@@ -43,10 +37,7 @@ def get_model_leaderboard_df(results_path: str, requests_path: str="", cols: lis
         # print(col)
         # if 'Std dev' in col or 'Score' in col:
         if 'Std dev' in col or 'Score' in col:
-            if "Chemistry" in col:
-                df[col] = (df[col]).map('{:.2f}'.format)
-            else:
-                df[col] = (df[col]*100).map('{:.2f}'.format)
+            df[col] = (df[col]*100).map('{:.2f}'.format)
             # df[col] = df[col].round(decimals=2)
 
     # df = df.sort_values(by=[AutoEvalColumn.score.name], ascending=True)

src/results/models_2024-10-09-05:17:38.810960.json

@@ -1,2327 +0,0 @@
-[
-    {
-        "config": {
-            "model_name": "ChatGPT-4o-latest (2024-09-03)",
-            "organization": "OpenAI",
-            "license": "Proprietary",
-            "knowledge_cutoff": "2023/10"
-        },
-        "results": {
-            "OVERALL": {
-                "Average Score": 0.974329609,
-                "Standard Deviation": 0.005024959031,
-                "Rank": 2
-            },
-            "Geometry": {
-                "Average Score": 0.976028578,
-                "Standard Deviation": 0.01507912373,
-                "Rank": 3
-            },
-            "Algebra": {
-                "Average Score": 0.951199453,
-                "Standard Deviation": 0.08452452108,
-                "Rank": 3
-            },
-            "Probability": {
-                "Average Score": 0.842116641,
-                "Standard Deviation": 0.006267759054,
-                "Rank": 3
-            },
-            "Logical": {
-                "Average Score": 0.828490728,
-                "Standard Deviation": 0.009134213144,
-                "Rank": 3
-            },
-            "Social": {
-                "Average Score": 0.815902987,
-                "Standard Deviation": 0.0196254222,
-                "Rank": 4
-            },
-            "Chemistry": {
-                "Average Score": 100.0,
-                "Standard Deviation": null,
-                "Rank": 1
-            }
-        }
-    },
-    {
-        "config": {
-            "model_name": "gpt-4o-2024-08-06",
-            "organization": "OpenAI",
-            "license": "Proprietary",
-            "knowledge_cutoff": "2023/10"
-        },
-        "results": {
-            "OVERALL": {
-                "Average Score": 0.846571548,
-                "Standard Deviation": 0.03394056554,
-                "Rank": 6
-            },
-            "Geometry": {
-                "Average Score": 0.99773096,
-                "Standard Deviation": 0.002835555172,
-                "Rank": 1
-            },
-            "Algebra": {
-                "Average Score": 1.0,
-                "Standard Deviation": 0.0,
-                "Rank": 1
-            },
-            "Probability": {
-                "Average Score": 0.78855795,
-                "Standard Deviation": 0.008188675452,
-                "Rank": 6
-            },
-            "Logical": {
-                "Average Score": 0.668635768,
-                "Standard Deviation": 0.03466314094,
-                "Rank": 11
-            },
-            "Social": {
-                "Average Score": 0.680417314,
-                "Standard Deviation": 0.00656867063,
-                "Rank": 9
-            },
-            "Chemistry": {
-                "Average Score": 92.43090226400756,
-                "Standard Deviation": null,
-                "Rank": 2
-            }
-        }
-    },
-    {
-        "config": {
-            "model_name": "gpt-4o-2024-05-13",
-            "organization": "OpenAI",
-            "license": "Proprietary",
-            "knowledge_cutoff": "2023/10"
-        },
-        "results": {
-            "OVERALL": {
-                "Average Score": 0.846334477,
-                "Standard Deviation": 0.09377911572,
-                "Rank": 7
-            },
-            "Geometry": {
-                "Average Score": 0.972472377,
-                "Standard Deviation": 0.01648274205,
-                "Rank": 4
-            },
-            "Algebra": {
-                "Average Score": 0.995511298,
-                "Standard Deviation": 0.004097802515,
-                "Rank": 2
-            },
-            "Probability": {
-                "Average Score": 0.812149974,
-                "Standard Deviation": 0.007669585485,
-                "Rank": 4
-            },
-            "Logical": {
-                "Average Score": 0.755019692,
-                "Standard Deviation": 0.008149588572,
-                "Rank": 6
-            },
-            "Social": {
-                "Average Score": 0.609875087,
-                "Standard Deviation": 0.038729239,
-                "Rank": 14
-            },
-            "Chemistry": {
-                "Average Score": 79.1592634699295,
-                "Standard Deviation": null,
-                "Rank": 7
-            }
-        }
-    },
-    {
-        "config": {
-            "model_name": "gpt-4-turbo-2024-04-09",
-            "organization": "OpenAI",
-            "license": "Proprietary",
-            "knowledge_cutoff": "2023/12"
-        },
-        "results": {
-            "OVERALL": {
-                "Average Score": 0.855357972,
-                "Standard Deviation": 0.1016986368,
-                "Rank": 4
-            },
-            "Geometry": {
-                "Average Score": 0.95374588,
-                "Standard Deviation": 0.03109307166,
-                "Rank": 5
-            },
-            "Algebra": {
-                "Average Score": 0.930945223,
-                "Standard Deviation": 0.06705136813,
-                "Rank": 4
-            },
-            "Probability": {
-                "Average Score": 0.750705448,
-                "Standard Deviation": 0.05944483103,
-                "Rank": 8
-            },
-            "Logical": {
-                "Average Score": 0.77906699,
-                "Standard Deviation": 0.007406734161,
-                "Rank": 4
-            },
-            "Social": {
-                "Average Score": 0.715935163,
-                "Standard Deviation": 0.1209141409,
-                "Rank": 7
-            },
-            "Chemistry": {
-                "Average Score": 70.73143363230263,
-                "Standard Deviation": null,
-                "Rank": 12
-            }
-        }
-    },
-    {
-        "config": {
-            "model_name": "gemini-1.5-pro-001",
-            "organization": "Google",
-            "license": "Proprietary",
-            "knowledge_cutoff": "2023/11"
-        },
-        "results": {
-            "OVERALL": {
-                "Average Score": 0.797187842,
-                "Standard Deviation": 0.0272375249,
-                "Rank": 10
-            },
-            "Geometry": {
-                "Average Score": 0.9947169,
-                "Standard Deviation": 0.009150597621,
-                "Rank": 2
-            },
-            "Algebra": {
-                "Average Score": 0.857464301,
-                "Standard Deviation": 0.05014285338,
-                "Rank": 5
-            },
-            "Probability": {
-                "Average Score": 0.651781767,
-                "Standard Deviation": 0.04156998547,
-                "Rank": 12
-            },
-            "Logical": {
-                "Average Score": 0.739745471,
-                "Standard Deviation": 0.01631532019,
-                "Rank": 7
-            },
-            "Social": {
-                "Average Score": 0.649601885,
-                "Standard Deviation": 0.104854889,
-                "Rank": 12
-            }
-        }
-    },
-    {
-        "config": {
-            "model_name": "qwen2-72b-instruct",
-            "organization": "Alibaba",
-            "license": "Qianwen LICENSE",
-            "knowledge_cutoff": "2024/09"
-        },
-        "results": {
-            "OVERALL": {
-                "Average Score": 0.737918558,
-                "Standard Deviation": 0.09069077339,
-                "Rank": 11
-            },
-            "Geometry": {
-                "Average Score": 0.796870305,
-                "Standard Deviation": 0.0509025346,
-                "Rank": 9
-            },
-            "Algebra": {
-                "Average Score": 0.836194231,
-                "Standard Deviation": 0.04517093028,
-                "Rank": 6
-            },
-            "Probability": {
-                "Average Score": 0.788068004,
-                "Standard Deviation": 0.007288989044,
-                "Rank": 7
-            },
-            "Logical": {
-                "Average Score": 0.619300904,
-                "Standard Deviation": 0.06377931612,
-                "Rank": 15
-            },
-            "Social": {
-                "Average Score": 0.652578786,
-                "Standard Deviation": 0.04259293171,
-                "Rank": 11
-            },
-            "Chemistry": {
-                "Average Score": 73.54037778797029,
-                "Standard Deviation": null,
-                "Rank": 8
-            }
-        }
-    },
-    {
-        "config": {
-            "model_name": "gpt-4o-mini-2024-07-18",
-            "organization": "OpenAI",
-            "license": "Proprietary",
-            "knowledge_cutoff": "2023/10"
-        },
-        "results": {
-            "OVERALL": {
-                "Average Score": 0.847694133,
-                "Standard Deviation": 0.02164304402,
-                "Rank": 5
-            },
-            "Geometry": {
-                "Average Score": 0.946650435,
-                "Standard Deviation": 0.01831236482,
-                "Rank": 7
-            },
-            "Algebra": {
-                "Average Score": 0.796243022,
-                "Standard Deviation": 0.05537539202,
-                "Rank": 7
-            },
-            "Probability": {
-                "Average Score": 0.798402685,
-                "Standard Deviation": 0.009404491967,
-                "Rank": 5
-            },
-            "Logical": {
-                "Average Score": 0.727009735,
-                "Standard Deviation": 0.02628110141,
-                "Rank": 8
-            },
-            "Social": {
-                "Average Score": 0.691949855,
-                "Standard Deviation": 0.02072934333,
-                "Rank": 8
-            },
-            "Chemistry": {
-                "Average Score": 88.3877070580296,
-                "Standard Deviation": null,
-                "Rank": 3
-            }
-        }
-    },
-    {
-        "config": {
-            "model_name": "claude-3.5-sonnet",
-            "organization": "Anthropic",
-            "license": "Proprietary",
-            "knowledge_cutoff": "2024/04"
-        },
-        "results": {
-            "OVERALL": {
-                "Average Score": 0.839004422,
-                "Standard Deviation": 0.1461079564,
-                "Rank": 8
-            },
-            "Geometry": {
-                "Average Score": 0.95316419,
-                "Standard Deviation": 0.02081192856,
-                "Rank": 6
-            },
-            "Algebra": {
-                "Average Score": 0.759789952,
-                "Standard Deviation": 0.02611765096,
-                "Rank": 8
-            },
-            "Probability": {
-                "Average Score": 0.707730127,
-                "Standard Deviation": 0.0394436664,
-                "Rank": 10
-            },
-            "Logical": {
-                "Average Score": 0.77342666,
-                "Standard Deviation": 0.002892426458,
-                "Rank": 5
-            },
-            "Social": {
-                "Average Score": 0.790002247,
-                "Standard Deviation": 0.1007410022,
-                "Rank": 5
-            },
-            "Chemistry": {
-                "Average Score": 82.37734076815008,
-                "Standard Deviation": null,
-                "Rank": 6
-            }
-        }
-    },
-    {
-        "config": {
-            "model_name": "o1-mini",
-            "organization": "OpenAI",
-            "license": "Proprietary",
-            "knowledge_cutoff": "2023/10"
-        },
-        "results": {
-            "OVERALL": {
-                "Average Score": 1.0,
-                "Standard Deviation": 0.0,
-                "Rank": 1
-            },
-            "Geometry": {
-                "Average Score": "N/A",
-                "Standard Deviation": "N/A",
-                "Rank": "N/A"
-            },
-            "Algebra": {
-                "Average Score": "N/A",
-                "Standard Deviation": "N/A",
-                "Rank": "N/A"
-            },
-            "Probability": {
-                "Average Score": 1.0,
-                "Standard Deviation": 0.0,
-                "Rank": 1
-            },
-            "Logical": {
-                "Average Score": 1.0,
-                "Standard Deviation": 0.0,
-                "Rank": 1
-            },
-            "Social": {
-                "Average Score": 0.993974241,
-                "Standard Deviation": 0.001996882328,
-                "Rank": 2
-            }
-        }
-    },
-    {
-        "config": {
-            "model_name": "o1-preview",
-            "organization": "OpenAI",
-            "license": "Proprietary",
-            "knowledge_cutoff": "2023/10"
-        },
-        "results": {
-            "OVERALL": {
-                "Average Score": 0.945884589,
-                "Standard Deviation": 0.01059250762,
-                "Rank": 3
-            },
-            "Geometry": {
-                "Average Score": "N/A",
-                "Standard Deviation": "N/A",
-                "Rank": "N/A"
-            },
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