Adding verbal-reasoning-challenge as a Community Task

community_tasks/verbal_reasoning_challenge.py

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+# MIT License
+
+# Copyright (c) 2024 The HuggingFace Team
+
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+# ruff: noqa: F405, F403, F401
+"""
+Task to evaluate LLMs on the verbal reasoning challenge dataset:
+https://huggingface.co/datasets/nuprl/verbal-reasoning-challenge
+
+"""
+
+import re
+from typing import List
+
+import numpy as np
+from aenum import extend_enum
+
+from lighteval.metrics.metrics import Metrics, SampleLevelMetric
+from lighteval.metrics.utils.metric_utils import MetricCategory, MetricUseCase
+from lighteval.tasks.lighteval_task import LightevalTaskConfig
+from lighteval.tasks.requests import Doc
+
+
+def verbal_prompt_fn(line, task_name: str = None):
+    return Doc(
+        task_name=task_name,
+        query=line["challenge"],
+        choices=[line["answer"]],
+        gold_index=0,
+        specific={"ID": line["ID"]},
+    )
+
+
+def _parse_answer(text: str) -> List[List[str]]:
+    """
+    Converts text to lowercase. Then interprets ";" as a separator between
+    alternatives. Within each alternative, interprets "," and "-->" as separators
+    for elements of a set. Within each set, drops all non-alphanumeric characters
+    and returns that set.
+
+    Another way to describe this is that we interpret adjacent words as
+    phrases that must be present literally. However, comma and arrow separate
+    distinct phrases that may be present in any order. All other characters
+    are dropped.
+    """
+    text = text.lower()
+    alternatives = re.split(r";", text)
+    result = []
+    for alternative in alternatives:
+        groups = re.split(r"–?-?-?>|,", alternative)
+        result.append([" ".join(re.findall(r"\b\w+\b", group)) for group in groups])
+    return result
+
+
+def _answer_without_thoughts(completion: str) -> str:
+    if "<think>" not in completion[:200]:
+        return completion
+
+    chunks = completion.split("</think>")
+    if len(chunks) <= 1:
+        return ""
+
+    return chunks[-1].strip()
+
+
+def _check_answer(completion: str, answer: str) -> bool:
+    """
+    Check that all the phrases that must appear in the answer appear in the
+    completion. We ignore "thoughts", capitalization, and punctuation.
+    """
+    completion = _answer_without_thoughts(completion).lower()
+    completion = re.sub(r"[^\w\s]", " ", completion)
+    completion = re.sub(r"\s+", " ", completion)
+    alternative_answers = _parse_answer(answer)
+    for answer_phrases in alternative_answers:
+        if all(re.search(rf"\b{re.escape(phrase)}\b", completion) for phrase in answer_phrases):
+            return True
+    return False
+
+
+def verbal_metric(predictions: list[str], formatted_doc: Doc, **kwargs) -> bool:
+    completion = predictions[0]
+    answer = formatted_doc.choices[formatted_doc.gold_index]
+    return _check_answer(completion, answer)
+
+
+verbal_custom_metric = SampleLevelMetric(
+    metric_name="Verbal_Metric",
+    higher_is_better=True,
+    category=MetricCategory.GENERATIVE,
+    use_case=MetricUseCase.ACCURACY,
+    sample_level_fn=verbal_metric,
+    corpus_level_fn=np.mean,
+)
+
+
+task = LightevalTaskConfig(
+    name="verbal_reasoning_challenge",
+    prompt_function=verbal_prompt_fn,
+    suite=["community"],
+    hf_repo="nuprl/verbal-reasoning-challenge",
+    hf_subset="default",
+    hf_avail_splits=["test"],
+    evaluation_splits=["test"],
+    few_shots_split=None,
+    few_shots_select=None,
+    generation_size=2048,
+    metric=[verbal_custom_metric],
+)
+
+TASKS_TABLE = [task]
+
+extend_enum(Metrics, "verbal_custom_metric", verbal_custom_metric)