refine

llama_stack/providers/inline/scoring/basic/scoring_fn/ifeval_scoring_fn.py

@@ -6,7 +6,7 @@
 from typing import Any, Dict, Optional
 
 from llama_stack.apis.scoring import ScoringResultRow
-from llama_stack.apis.scoring_functions import ScoringFnParams, ScoringFnParamsType
+from llama_stack.apis.scoring_functions import ScoringFnParams
 from llama_stack.providers.utils.scoring.base_scoring_fn import RegisteredBaseScoringFn
 
 from ..utils.ifeval_utils import INSTRUCTION_DICT, INSTRUCTION_LIST
@@ -37,10 +37,6 @@ class IfEvalScoringFn(RegisteredBaseScoringFn):
         if scoring_params is not None:
             fn_def.params = scoring_params
 
-        assert fn_def.params is not None and fn_def.params.type == ScoringFnParamsType.regex_parser.value, (
-            f"RegexParserScoringFnParams not found for {fn_def}."
-        )
-
         instruction_list = input_row["instruction_id_list"]
         generated_answer = input_row["generated_answer"].strip()
 
@@ -56,7 +52,9 @@ class IfEvalScoringFn(RegisteredBaseScoringFn):
             results[instruction_id + "_total"] += 1.0
             results[instruction_id.split(":")[0] + "_total"] += 1.0
 
-            instruction.build_description(**input_row["kwargs"][index])
+            clean_input_row = {k: v for k, v in input_row["kwargs"][index].items() if v is not None}
+            print(clean_input_row)
+            instruction.build_description(**clean_input_row)
             args = instruction.get_instruction_args()
             if args and "prompt" in args:
                 instruction.build_description(prompt=input_row["prompt"])

llama_stack/providers/inline/scoring/basic/utils/ifeval_utils.py

@@ -3147,7 +3147,7 @@ class LowercaseLettersEnglishChecker(Instruction):
 class CommaChecker(Instruction):
     """Checks the response for no commas."""
 
-    def build_description(self):
+    def build_description(self, **kwargs):
         """Build the instruction description."""
         self._description_pattern = "In your entire response, refrain from the use of any commas."
         return self._description_pattern
@@ -3216,6 +3216,7 @@ class CapitalWordFrequencyChecker(Instruction):
     def check_following(self, value):
         """Checks the frequency of words with all capital letters."""
         # Hyphenated words will count as one word
+        nltk.download("punkt_tab")
         words = nltk.word_tokenize(value)
         capital_words = [word for word in words if word.isupper()]
 

llama_stack/providers/utils/scoring/aggregation_utils.py

@@ -57,6 +57,7 @@ def aggregate_median(scoring_results: List[ScoringResultRow]) -> Dict[str, Any]:
 AGGREGATION_FUNCTIONS = {
     AggregationFunctionType.accuracy: aggregate_accuracy,
     AggregationFunctionType.average: aggregate_average,
+    AggregationFunctionType.weighted_average: aggregate_weighted_average,
     AggregationFunctionType.categorical_count: aggregate_categorical_count,
     AggregationFunctionType.median: aggregate_median,
 }

llama_stack/templates/open-benchmark/open_benchmark.py

@@ -204,8 +204,7 @@ def get_distribution_template() -> DistributionTemplate:
             ),
         ),
         DatasetInput(
-            dataset_id="IfEval",
-            provider_id="huggingface",
+            dataset_id="ifeval",
             purpose=DatasetPurpose.eval_messages_answer,
             source=URIDataSource(
                 uri="huggingface://datasets/llamastack/IfEval?split=train",
@@ -240,9 +239,9 @@ def get_distribution_template() -> DistributionTemplate:
             scoring_functions=["basic::bfcl"],
         ),
         BenchmarkInput(
-            benchmark_id="meta-reference-IfEval",
-            dataset_id="IfEval",
-            scoring_functions=["basic::IfEval"],
+            benchmark_id="meta-reference-ifeval",
+            dataset_id="ifeval",
+            scoring_functions=["basic::ifeval"],
         ),
     ]
     return DistributionTemplate(

llama_stack/templates/open-benchmark/run.yaml

@@ -193,7 +193,7 @@ datasets:
     type: uri
     uri: huggingface://datasets/llamastack/IfEval?split=train
   metadata: {}
-  dataset_id: IfEval
+  dataset_id: ifeval
 scoring_fns: []
 benchmarks:
 - dataset_id: simpleqa
@@ -221,11 +221,11 @@ benchmarks:
   - basic::bfcl
   metadata: {}
   benchmark_id: meta-reference-bfcl
-- dataset_id: IfEval
+- dataset_id: ifeval
   scoring_functions:
-  - basic::IfEval
+  - basic::ifeval
   metadata: {}
-  benchmark_id: meta-reference-IfEval
+  benchmark_id: meta-reference-ifeval
 tool_groups:
 - toolgroup_id: builtin::websearch
   provider_id: tavily-search