Datasets:

danidarko

/

docs

like 0

reference = clean(docs['span1_text'])

if ("'" in prediction) != ("'" in reference):

predicted_referent = False

else:

prediction_words = set(prediction.split(' '))

referent_words = set(reference.split(' '))

predicted_referent = prediction_words.issubset(referent_words) or referent_words.issubset(prediction_words)

acc = 1.0 if predicted_referent == docs['label'] else 0.0

return {'accuracy': acc}

# File: lm-evaluation-harness-main/lm_eval/tasks/swde/task.py

import re

from typing import List

import numpy as np

from lm_eval.api.instance import Instance

from lm_eval.api.task import ConfigurableTask

class SWDE(ConfigurableTask):

VERSION = 0

DATASET_PATH = 'hazyresearch/based-swde-v2'

DATASET_NAME = 'default'

def __init__(self, **kwargs):

super().__init__(config={'metadata': {'version': self.VERSION}})

def has_training_docs(self):

return False

def has_validation_docs(self):

return True

def has_test_docs(self):

return False

def validation_docs(self):

return self.dataset['validation']

def doc_to_text(self, doc):

return doc['text']

def doc_to_target(self, doc):

return doc['value']

def construct_requests(self, doc, ctx, **kwargs):

return [Instance(request_type='generate_until', doc=doc, arguments=(ctx, {'until': ['\n'], 'max_gen_toks': 48}), idx=0, **kwargs)]

def process_results(self, doc, results):

continuation = results

return {'contains': contains_score(continuation[0], [doc['value']])}

def aggregation(self):

return {'contains': np.mean}

def higher_is_better(self):

return {'contains': True}

def contains_score(prediction: str, labels: List[str]):

return max((int(bool(re.search(re.compile(re.escape(label), re.IGNORECASE), prediction))) for label in labels))

# File: lm-evaluation-harness-main/lm_eval/tasks/tinyBenchmarks/agg_functions.py

from typing import List

import numpy as np

try:

import tinyBenchmarks as tb

except ModuleNotFoundError:

raise ModuleNotFoundError('`tinyBenchmarks` is required for tinyBenchmarks task metric calculation, install via `pip install git+https://github.com/felipemaiapolo/tinyBenchmarks`')

def agg_pirt(items: List[float], benchmark: str) -> float:

items = np.array(items)

predictions = tb.evaluate(items, benchmark)

return predictions[benchmark]['pirt']

def agg_gpirt_arc(items: List[float], benchmark: str='arc') -> float:

items = np.array(items)

predictions = tb.evaluate(items, benchmark)

return predictions[benchmark]['gpirt']

def agg_gpirt_gsm8k(items: List[float], benchmark: str='gsm8k') -> float:

items = np.array(items)

predictions = tb.evaluate(items, benchmark)

return predictions[benchmark]['gpirt']

def agg_gpirt_hellaswag(items: List[float], benchmark: str='hellaswag') -> float:

items = np.array(items)

predictions = tb.evaluate(items, benchmark)

return predictions[benchmark]['gpirt']

def agg_gpirt_mmlu(items: List[float], benchmark: str='mmlu') -> float:

items = np.array(items)

predictions = tb.evaluate(items, benchmark)

return predictions[benchmark]['gpirt']

def agg_gpirt_truthfulqa(items: List[float], benchmark: str='truthfulqa') -> float:

items = np.array(items)

predictions = tb.evaluate(items, benchmark)

return predictions[benchmark]['gpirt']

def agg_gpirt_winogrande(items: List[float], benchmark: str='winogrande') -> float:

items = np.array(items)

predictions = tb.evaluate(items, benchmark)