class_docstr: Summarization Metrics
  
  This class computes the following standard summarization metrics
  
  1. Rouge (1,2,L)
  2. Extractiveness (coverage, density, novel n-grams)
  3. Compression
  4. Faithfulness (SummaC)
docstr: Load all human evaluation data cached on CodaLab into a single dictionary
  
  key: (metric_type: str, model_name: str, output_summary: str)
  value: corresponding score: float
code: def _load_humaneval(self, eval_cache_path: str) -> Dict:
      """
      Load all human evaluation data cached on CodaLab into a single dictionary
  
      key: (metric_type: str, model_name: str, output_summary: str)
      value: corresponding score: float
      """
      if "cnndm" in self.task:
          dataset = "cnndm"
      elif "xsum" in self.task:
          dataset = "xsum"
      else:
          raise ValueError
  
      all_humaneval_scores = dict()
      for shots in [0, 5]:
          score_analyzer = SummarizationHumanEvalAnalyzer(
              dataset, eval_cache_path, shots=shots
          )
          for (
              model_name,
              input_id,
              output_text,
          ), score in score_analyzer.faithfulness_full.items():
              if isinstance(output_text, float):
                  output_text = ""
              all_humaneval_scores[
                  ("faithfulness", model_name, input_id, output_text)
              ] = score
          for (
              model_name,
              input_id,
              output_text,
          ), score in score_analyzer.relevance_full.items():
              if isinstance(output_text, float):
                  output_text = ""
              all_humaneval_scores[
                  ("relevance", model_name, input_id, output_text)
              ] = score
          for (
              model_name,
              input_id,
              output_text,
          ), score in score_analyzer.coherence_full.items():
              if isinstance(output_text, float):
                  output_text = ""
              all_humaneval_scores[
                  ("coherence", model_name, input_id, output_text)
              ] = score
      return all_humaneval_scores
  
imports: import json
  import pandas
  import numpy
  import os
  import pickle
  import spacy
  from typing import List, Dict, Optional
  from collections import defaultdict
  from helm.benchmark.adaptation.scenario_state import ScenarioState
  from helm.benchmark.adaptation.request_state import RequestState
  from helm.benchmark.adaptation.adapter_spec import AdapterSpec
  from helm.common.hierarchical_logger import hlog
  from helm.common.general import ensure_file_downloaded
  from .metric import Metric, MetricResult
  from .metric_name import MetricName
  from .metric_service import MetricService
  from .basic_metrics import get_rouge_function
  from .statistic import Stat
  from .summac.model_summac import SummaCZS
  from bert_score import BERTScorer
label_desc: Use image node_id 3 for calling the SummarizationMetric obj's underlying member method code with example usage: obj._load_humaneval(eval_cache_path) and returns: all_humaneval_scores