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update:update xyz DataFormat rule #512

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Merged
e06084 merged 1 commit into from
Oct 9, 2026
Merged

update:update xyz DataFormat rule #512

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36 changes: 15 additions & 21 deletions dingo/model/rule/rule_common.py
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Original file line number Diff line number Diff line change
Expand Up @@ -171,20 +171,19 @@ class RuleAudioDataFormat(BaseRule):
"category": "Rule-Based TEXT Quality Metrics",
"quality_dimension": "EFFECTIVENESS",
"metric_name": "RuleAudioDataFormat",
"description": "Check whether the audio data format is right",
"description": "Check whether the audio data format is right, include id, audio, text fields",
"evaluation_results": ""
}

_required_fields = [RequiredField.CONTENT]
dynamic_config = EvaluatorRuleArgs()

@classmethod
def eval(cls, input_data: Data) -> EvalDetail:
res = EvalDetail(metric=cls.__name__)

raw_data = input_data.raw_data
record = input_data.to_dict()
key_list = ["id", "audio", "text"]
if all(key in raw_data for key in key_list):
if all(key in record for key in key_list):
res.label = [QualityLabel.QUALITY_GOOD]
else:
res.status = True
Expand Down Expand Up @@ -1305,20 +1304,19 @@ class RuleImageDataFormat(BaseRule):
"category": "Rule-Based TEXT Quality Metrics",
"quality_dimension": "EFFECTIVENESS",
"metric_name": "RuleImageDataFormat",
"description": "Check whether the image data format is right",
"description": "Check whether the image data format is right, include img_id, image fields",
"evaluation_results": ""
}

_required_fields = [RequiredField.CONTENT]
dynamic_config = EvaluatorRuleArgs()

@classmethod
def eval(cls, input_data: Data) -> EvalDetail:
res = EvalDetail(metric=cls.__name__)

raw_data = input_data.raw_data
record = input_data.to_dict()
key_list = ["img_id", "image"]
if all(key in raw_data for key in key_list):
if all(key in record for key in key_list):
res.label = [QualityLabel.QUALITY_GOOD]
else:
res.status = True
Expand Down Expand Up @@ -1655,20 +1653,19 @@ class RuleNlpDataFormat(BaseRule):
"category": "Rule-Based TEXT Quality Metrics",
"quality_dimension": "EFFECTIVENESS",
"metric_name": "RuleNlpDataFormat",
"description": "Check whether the nlp data format is right",
"description": "Check whether the nlp data format is right, include track_id, content fields",
"evaluation_results": ""
}

_required_fields = [RequiredField.CONTENT]
dynamic_config = EvaluatorRuleArgs()

@classmethod
def eval(cls, input_data: Data) -> EvalDetail:
res = EvalDetail(metric=cls.__name__)

raw_data = input_data.raw_data
record = input_data.to_dict()
key_list = ["track_id", "content"]
if all(key in raw_data for key in key_list):
if all(key in record for key in key_list):
res.label = [QualityLabel.QUALITY_GOOD]
else:
res.status = True
Expand Down Expand Up @@ -1826,20 +1823,19 @@ class RuleSftDataFormat(BaseRule):
"category": "Rule-Based TEXT Quality Metrics",
"quality_dimension": "EFFECTIVENESS",
"metric_name": "RuleSftDataFormat",
"description": "Check whether the sft data format is right",
"description": "Check whether the sft data format is right, include track_id, type, prompt, completion fields",
"evaluation_results": ""
}

_required_fields = [RequiredField.CONTENT]
dynamic_config = EvaluatorRuleArgs()

@classmethod
def eval(cls, input_data: Data) -> EvalDetail:
res = EvalDetail(metric=cls.__name__)

raw_data = input_data.raw_data
record = input_data.to_dict()
key_list = ["track_id", "type", "prompt", "completion"]
if all(key in raw_data for key in key_list):
if all(key in record for key in key_list):
res.label = [QualityLabel.QUALITY_GOOD]
else:
res.status = True
Expand Down Expand Up @@ -2204,19 +2200,17 @@ class RuleVedioDataFormat(BaseRule):
"category": "Rule-Based TEXT Quality Metrics",
"quality_dimension": "EFFECTIVENESS",
"metric_name": "RuleVedioDataFormat",
"description": "Check whether the vedio data format is right",
"description": "Check whether the vedio data format is right, include id, video, text fields",
"evaluation_results": ""
}
dynamic_config = EvaluatorRuleArgs()

_required_fields = [RequiredField.CONTENT]

@classmethod
def eval(cls, input_data: Data) -> EvalDetail:
res = EvalDetail(metric=cls.__name__)
raw_data = input_data.raw_data
record = input_data.to_dict()
key_list = ["id", "video", "text"]
if all(key in raw_data for key in key_list):
if all(key in record for key in key_list):
res.label = [QualityLabel.QUALITY_GOOD]
else:
res.status = True
Expand Down
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