feat: update auto-gen-metric (MigoXLab#289)

* feat: update auto-gen-metric

* 📚 Auto-update metrics documentation

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Co-authored-by: GitHub Action <action@github.com>

Author: 2 people

dingo/model/llm/llm_keyword_matcher.py

@@ -89,7 +89,7 @@ class LLMKeywordMatcher(BaseOpenAI):
     """
 
     _metric_info = {
-        "category": "Resume ATS Matching Metrics",
+        "category": "Resume Quality Assessment Metrics",
         "metric_name": "LLMKeywordMatcher",
         "description": "Semantic keyword matching between resume and job description",
         "paper_title": "N/A",

dingo/model/llm/llm_resume_optimizer.py

@@ -39,7 +39,7 @@ class LLMResumeOptimizer(BaseOpenAI):
     """
 
     _metric_info = {
-        "category": "Resume ATS Optimization Metrics",
+        "category": "Resume Quality Assessment Metrics",
         "metric_name": "LLMResumeOptimizer",
         "description": "ATS-focused resume optimization with keyword injection and STAR polishing",
         "paper_title": "N/A",

dingo/model/rule/rule_xinghe.py

@@ -10,7 +10,7 @@
 @Model.rule_register("QUALITY_BAD_EFFECTIVENESS", ["xinghe"])
 class RuleDoi(BaseRule):
     _metric_info = {
-        "category": "Xinghe Data Quality Metrics",
+        "category": "Rule-Based TEXT Quality Metrics",
         "quality_dimension": "EFFECTIVENESS",
         "metric_name": "RuleDoi",
         "description": "Check whether the string is in the correct format of the doi",
@@ -38,7 +38,7 @@ def eval(cls, input_data: Data) -> EvalDetail:
 @Model.rule_register("QUALITY_BAD_EFFECTIVENESS", ["xinghe"])
 class RuleIsbn(BaseRule):
     _metric_info = {
-        "category": "Xinghe Data Quality Metrics",
+        "category": "Rule-Based TEXT Quality Metrics",
         "quality_dimension": "EFFECTIVENESS",
         "metric_name": "RuleIsbn",
         "description": "Check whether the string is in the correct format of the isbn",