📄 Ensemble V1 Ibmgenai Judges¶
A pre-trained Random Forest-based ensemble judge to assesss multi-turn conversation quality on Answer topicality: Response of the model only contains information that is related to and helpful for the user inquiry.
metrics.llm_as_judge.conversation_answer_topicality.ensemble_v1_ibmgenai_judges
type: RandomForestMetricsEnsemble
metrics:
- type: LLMAsJudge
inference_model:
type: IbmGenAiInferenceEngine
model_name: meta-llama/llama-3-70b-instruct
parameters:
type: IbmGenAiInferenceEngineParams
max_new_tokens: 256
template: templates.response_assessment.judges.topicality.v3
task: rating.single_turn
main_score: metrics.llm_as_judge.rating.llama370binstruct_template_v3
prediction_type: str
- type: LLMAsJudge
inference_model:
type: IbmGenAiInferenceEngine
model_name: mistralai/mixtral-8x7b-instruct-v01
parameters:
type: IbmGenAiInferenceEngineParams
max_new_tokens: 256
template: templates.response_assessment.judges.topicality.v3
task: rating.single_turn
main_score: metrics.llm_as_judge.rating.mixtral8x7binstructv01_template_v3
prediction_type: str
- type: LLMAsJudge
inference_model:
type: IbmGenAiInferenceEngine
model_name: ibm/granite-13b-instruct-v2
parameters:
type: IbmGenAiInferenceEngineParams
max_new_tokens: 256
template: templates.response_assessment.judges.topicality.v3
task: rating.single_turn
main_score: metrics.llm_as_judge.rating.granite13binstructv2_template_v3
prediction_type: str
- type: LLMAsJudge
inference_model:
type: IbmGenAiInferenceEngine
model_name: ibm/granite-13b-chat-v2
parameters:
type: IbmGenAiInferenceEngineParams
max_new_tokens: 256
template: templates.response_assessment.judges.topicality.v3
task: rating.single_turn
main_score: metrics.llm_as_judge.rating.granite13bchatv2_template_v3
prediction_type: str
weights:
judges:
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- ibm/granite-13b-instruct-v2
- - templates.response_assessment.judges.topicality.v3
- ibm/granite-13b-chat-v2
platform_name: ibm_gen_ai
metric_name: topicality
version_number: v1
ensemble_model_name: random_forest
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min_samples_split: 2
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[source]Explanation about RandomForestMetricsEnsemble¶
This class extends the MetricsEnsemble base class and leverages a pre-trained scikit-learn Random Forest classification model to combine and aggregate scores from multiple judges.
- load_weights method:
Loads model weights from dictionary representation of a random forest classifier.
- ensemble method:
Decodes the RandomForestClassifier object and predict a score based on the given instance.
Explanation about LLMAsJudge¶
LLM-as-judge-based metric class for evaluating correctness of generated predictions.
This class uses the source prompt given to the generator and the generator’s predictions to evaluate correctness using one of three supported tasks (rating.single_turn, rating.single_turn_with_reference, pairwise_comparative_rating.single_turn).
- Attributes:
main_score (str): The main score label used for evaluation.
task (Literal[“rating.single_turn”,”rating.single_turn_with_reference”, “pairwise_comparative_rating.single_turn”]): The type of task the llm as judge runs. This defines the output and input format of the judge model.
template (Template): The template used when generating inputs for the judge llm.
format (Format): The format used when generating inputs for judge llm.
system_prompt (SystemPrompt): The system prompt used when generating inputs for judge llm.
strip_system_prompt_and_format_from_inputs (bool): Whether to strip the system prompt and formatting from the inputs that the models that is being judges received, when they are inserted to the llm-as-judge prompt.
inference_model (InferenceEngine): The module that creates the inference of the judge llm.
reduction_map (dict): A dictionary specifying the reduction method for the metric.
batch_size (int): The size of the bulk.
References: templates.response_assessment.judges.topicality.v3
Read more about catalog usage here.