Update README.md

README.md

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----
-license: apache-2.0
----
-
-# $OneMillion-Bench
-
-A bilingual (Global/Chinese) realistic expert-level benchmark for evaluating language agents across **5 professional domains**. The benchmark contains **400 entries** with detailed, weighted rubric-based grading criteria designed for fine-grained evaluation of domain expertise, analytical reasoning, and instruction following.
-
-## Dataset Structure
-
-Each subdirectory is a **Hugging Face subset** (configuration), and all data is in the **`test`** split.
-
-```
-$OneMillion-Bench/
-├── economics_and_finance/
-│   └── test.json      # 80 entries (40 EN + 40 CN, distinct questions)
-├── healthcare_and_medicine/
-│   └── test.json      # 80 entries (40 matched EN-CN pairs)
-├── industry/
-│   └── test.json      # 80 entries (40 matched EN-CN pairs)
-├── law/
-│   └── test.json      # 80 entries (40 EN + 40 CN, distinct questions)
-├── natural_science/
-│   └── test.json      # 80 entries (40 matched EN-CN pairs)
-└── README.md
-```
-
-| Subset | Split | Entries |
-|---|---|---|
-| `economics_and_finance` | `test` | 80 |
-| `healthcare_and_medicine` | `test` | 80 |
-| `industry` | `test` | 80 |
-| `law` | `test` | 80 |
-| `natural_science` | `test` | 80 |
-
-## Domains & Coverage
-
-| Domain | Categories | Example Subcategories | Bilingual Mode |
-|---|---|---|---|
-| **Economics & Finance** | Investing, FinTech, Banking, Insurance, M&A | Equities, VC/PE, Cryptocurrency, Commodities | Separate questions per language |
-| **Healthcare & Medicine** | Clinical Medicine, Basic Medicine, Pharma & Biotech | Hepatobiliary Surgery, Oncology, Nephrology, Dentistry | Matched translation pairs |
-| **Industry** | Telecommunications, ML, Architecture, Semiconductors | Backend Dev, Chemical Engineering, Chip Design | Matched translation pairs |
-| **Law** | Civil, Criminal, International, Corporate, IP, Labor | Contract Disputes, Criminal Defense, Copyright, M&A | Separate questions per language |
-| **Natural Science** | Chemistry, Biology, Physics, Mathematics | Organic Chemistry, Condensed Matter, Molecular Biology | Matched translation pairs |
-
-## Entry Schema
-
-Each entry is a JSON object with 7 fields:
-
-```jsonc
-{
-  "id": "uuid-string",            // globally unique identifier
-  "case_id": 1,                   // links bilingual pairs (in matched-pair domains)
-  "language": "en",               // "en" or "cn" (50/50 split in every file)
-  "system_prompt": "",            // reserved (empty across all entries)
-  "question": "...",              // expert-level evaluation prompt
-  "tags": {
-    "topics": [                   // 3-level taxonomy
-      "Domain",                   //   e.g. "Economics and Finance"
-      "Category",                 //   e.g. "Investing"
-      "Subcategory"               //   e.g. "Equities"
-    ],
-    "time_sensitivity": {
-      "time_sensitivity": "Time-agnostic",   // or "Weakly/Strongly time-sensitive"
-      "year_month": "NA",                    // "YYYY-MM" when time-sensitive
-      "day": "NA"                            // "DD" when applicable
-    }
-  },
-  "rubrics": [                    // weighted grading criteria (11-37 per entry)
-    {
-      "rubric_number": 1,
-      "rubric_detail": "...",     // specific grading criterion
-      "rubric_weight": 5,         // positive = reward, negative = penalty
-      "rubric_label": "..."       // category (see below)
-    }
-  ]
-}
-```
-
-### Rubric Labels
-
-| Label | Role | Typical Weight |
-|---|---|---|
-| Factual Information | Tests factual accuracy | +3 to +5 |
-| Analytical Reasoning | Assesses depth of analysis | +3 to +5 |
-| Structure and Formatting | Evaluates output organization | -2 to -4 (penalty) |
-| Instructions Following | Checks compliance with task constraints | mixed |
-
-## Quick Start
-
-```python
-import json
-
-# Load a subset (test split)
-with open("natural_science/test.json") as f:
-    data = json.load(f)
-
-# Filter English entries
-en_entries = [e for e in data if e["language"] == "en"]
-
-# Iterate with rubrics
-for entry in en_entries[:1]:
-    print(f"Topic: {' > '.join(entry['tags']['topics'])}")
-    print(f"Question: {entry['question'][:200]}...")
-    print(f"Rubrics ({len(entry['rubrics'])}):")
-    for r in entry["rubrics"][:3]:
-        print(f"  [{r['rubric_weight']:+d}] {r['rubric_label']}: {r['rubric_detail'][:80]}...")
-```
-
-Example output:
-
-```
-Topic: Natural Sciences > Chemistry > Organic Chemistry
-Question: You are an expert in organic chemistry. A graduate student is researching ...
-Rubrics (18):
-  [+5] Factual Information: Correctly identifies the primary reaction mechanism ...
-  [+4] Analytical Reasoning: Provides a coherent comparison of thermodynamic vs ...
-  [-3] Structure and Formatting: Response lacks clear section headings or logica...
-```
-
-## Evaluation
-
-Score a model response by summing the weights of satisfied rubrics:
-
-```python
-def score(response: str, rubrics: list, judge_fn) -> dict:
-    """
-    judge_fn(response, rubric_detail) -> bool
-    """
-    total, earned = 0, 0
-    for r in rubrics:
-        met = judge_fn(response, r["rubric_detail"])
-        if met:
-            earned += r["rubric_weight"]
-        if r["rubric_weight"] > 0:
-            total += r["rubric_weight"]
-    return {"score": earned, "max_possible": total, "pct": earned / total if total else 0}
-```
-
-## License
-
-Apache 2.0
+---
+license: apache-2.0
+task_categories:
+- question-answering
+- text-generation
+language:
+- en
+- zh
+tags:
+- economics_and_finance
+- healthcare_and_medicine
+- industry
+- law
+- natural_science
+pretty_name: $OneMillion-Bench
+size_categories:
+- n<1K
+---
+
+# $OneMillion-Bench
+
+A bilingual (Global/Chinese) realistic expert-level benchmark for evaluating language agents across **5 professional domains**. The benchmark contains **400 entries** with detailed, weighted rubric-based grading criteria designed for fine-grained evaluation of domain expertise, analytical reasoning, and instruction following.
+
+## Dataset Structure
+
+Each subdirectory is a **Hugging Face subset** (configuration), and all data is in the **`test`** split.
+
+```
+$OneMillion-Bench/
+├── economics_and_finance/
+│   └── test.json      # 80 entries (40 EN + 40 CN, distinct questions)
+├── healthcare_and_medicine/
+│   └── test.json      # 80 entries (40 matched EN-CN pairs)
+├── industry/
+│   └── test.json      # 80 entries (40 matched EN-CN pairs)
+├── law/
+│   └── test.json      # 80 entries (40 EN + 40 CN, distinct questions)
+├── natural_science/
+│   └── test.json      # 80 entries (40 matched EN-CN pairs)
+└── README.md
+```
+
+| Subset | Split | Entries |
+|---|---|---|
+| `economics_and_finance` | `test` | 80 |
+| `healthcare_and_medicine` | `test` | 80 |
+| `industry` | `test` | 80 |
+| `law` | `test` | 80 |
+| `natural_science` | `test` | 80 |
+
+## Domains & Coverage
+
+| Domain | Categories | Example Subcategories | Bilingual Mode |
+|---|---|---|---|
+| **Economics & Finance** | Investing, FinTech, Banking, Insurance, M&A | Equities, VC/PE, Cryptocurrency, Commodities | Separate questions per language |
+| **Healthcare & Medicine** | Clinical Medicine, Basic Medicine, Pharma & Biotech | Hepatobiliary Surgery, Oncology, Nephrology, Dentistry | Matched translation pairs |
+| **Industry** | Telecommunications, ML, Architecture, Semiconductors | Backend Dev, Chemical Engineering, Chip Design | Matched translation pairs |
+| **Law** | Civil, Criminal, International, Corporate, IP, Labor | Contract Disputes, Criminal Defense, Copyright, M&A | Separate questions per language |
+| **Natural Science** | Chemistry, Biology, Physics, Mathematics | Organic Chemistry, Condensed Matter, Molecular Biology | Matched translation pairs |
+
+## Entry Schema
+
+Each entry is a JSON object with 7 fields:
+
+```jsonc
+{
+  "id": "uuid-string",            // globally unique identifier
+  "case_id": 1,                   // links bilingual pairs (in matched-pair domains)
+  "language": "en",               // "en" or "cn" (50/50 split in every file)
+  "system_prompt": "",            // reserved (empty across all entries)
+  "question": "...",              // expert-level evaluation prompt
+  "tags": {
+    "topics": [                   // 3-level taxonomy
+      "Domain",                   //   e.g. "Economics and Finance"
+      "Category",                 //   e.g. "Investing"
+      "Subcategory"               //   e.g. "Equities"
+    ],
+    "time_sensitivity": {
+      "time_sensitivity": "Time-agnostic",   // or "Weakly/Strongly time-sensitive"
+      "year_month": "NA",                    // "YYYY-MM" when time-sensitive
+      "day": "NA"                            // "DD" when applicable
+    }
+  },
+  "rubrics": [                    // weighted grading criteria (11-37 per entry)
+    {
+      "rubric_number": 1,
+      "rubric_detail": "...",     // specific grading criterion
+      "rubric_weight": 5,         // positive = reward, negative = penalty
+      "rubric_label": "..."       // category (see below)
+    }
+  ]
+}
+```
+
+### Rubric Labels
+
+| Label | Role | Typical Weight |
+|---|---|---|
+| Factual Information | Tests factual accuracy | +3 to +5 |
+| Analytical Reasoning | Assesses depth of analysis | +3 to +5 |
+| Structure and Formatting | Evaluates output organization | -2 to -4 (penalty) |
+| Instructions Following | Checks compliance with task constraints | mixed |
+
+## Quick Start
+
+```python
+import json
+
+# Load a subset (test split)
+with open("natural_science/test.json") as f:
+    data = json.load(f)
+
+# Filter English entries
+en_entries = [e for e in data if e["language"] == "en"]
+
+# Iterate with rubrics
+for entry in en_entries[:1]:
+    print(f"Topic: {' > '.join(entry['tags']['topics'])}")
+    print(f"Question: {entry['question'][:200]}...")
+    print(f"Rubrics ({len(entry['rubrics'])}):")
+    for r in entry["rubrics"][:3]:
+        print(f"  [{r['rubric_weight']:+d}] {r['rubric_label']}: {r['rubric_detail'][:80]}...")
+```
+
+Example output:
+
+```
+Topic: Natural Sciences > Chemistry > Organic Chemistry
+Question: You are an expert in organic chemistry. A graduate student is researching ...
+Rubrics (18):
+  [+5] Factual Information: Correctly identifies the primary reaction mechanism ...
+  [+4] Analytical Reasoning: Provides a coherent comparison of thermodynamic vs ...
+  [-3] Structure and Formatting: Response lacks clear section headings or logica...
+```
+
+## Evaluation
+
+Score a model response by summing the weights of satisfied rubrics:
+
+```python
+def score(response: str, rubrics: list, judge_fn) -> dict:
+    """
+    judge_fn(response, rubric_detail) -> bool
+    """
+    total, earned = 0, 0
+    for r in rubrics:
+        met = judge_fn(response, r["rubric_detail"])
+        if met:
+            earned += r["rubric_weight"]
+        if r["rubric_weight"] > 0:
+            total += r["rubric_weight"]
+    return {"score": earned, "max_possible": total, "pct": earned / total if total else 0}
+```
+
+## License
+
+Apache 2.0