Datasets:

danidarko

/

docs

like 0

except KeyError:

eval_logger.warning(f'{name} metric is not assigned a default aggregation!')

def is_higher_better(metric_name) -> bool:

try:

return HIGHER_IS_BETTER_REGISTRY[metric_name]

except KeyError:

eval_logger.warning(f"higher_is_better not specified for metric '{metric_name}'!")

def register_filter(name):

def decorate(cls):

if name in FILTER_REGISTRY:

eval_logger.info(f'Registering filter `{name}` that is already in Registry {FILTER_REGISTRY}')

FILTER_REGISTRY[name] = cls

return cls

return decorate

def get_filter(filter_name: str) -> type:

try:

return FILTER_REGISTRY[filter_name]

except KeyError:

eval_logger.warning(f'filter `{filter_name}` is not registered!')

# File: lm-evaluation-harness-main/lm_eval/api/samplers.py

import datasets

class ContextSampler:

def __init__(self, docs, task, fewshot_indices=None, rnd=None) -> None:

self.rnd = rnd

if not self.rnd:

raise ValueError('A `random.Random` generator argument must be provided to `rnd` of FewShotSampler!')

self.task = task

self.config = task._config

self.target_delimiter = self.config.target_delimiter

self.fewshot_delimiter = self.config.fewshot_delimiter

self.doc_to_text = self.task.doc_to_text

self.doc_to_target = self.task.doc_to_target

self.doc_to_choice = self.task.doc_to_choice

self.docs = docs

if fewshot_indices:

if not isinstance(self.docs, datasets.Dataset):

raise ValueError("Got `fewshot_indices` but fewshot_docs are not a HF dataset. Don't use both `fewshot_indices` and a user-defined few-shot sample list simultaneously")

self.docs = self.docs.select(fewshot_indices)

def get_context(self, doc, num_fewshot):

n_samples = num_fewshot + 1 if self.config.fewshot_split == self.config.test_split else num_fewshot

fewshotex = self.sample(n_samples)

selected_docs = [x for x in fewshotex if x != doc][:num_fewshot]

labeled_examples = ''

for doc in selected_docs:

doc_content = self.doc_to_text(doc)

doc_target = self.doc_to_target(doc)

labeled_examples += doc_content if self.config.doc_to_choice is None or isinstance(doc_content, str) else self.doc_to_choice(doc)[doc_content]

labeled_examples += self.target_delimiter

labeled_examples += str(doc_target[0]) if isinstance(doc_target, list) else str(doc_target) if self.config.doc_to_choice is None or isinstance(doc_target, str) else str(self.doc_to_choice(doc)[doc_target])

labeled_examples += self.fewshot_delimiter

return labeled_examples

def get_chat_context(self, doc, num_fewshot, fewshot_as_multiturn: bool=False):

chat_history = []

n_samples = num_fewshot + 1 if self.config.fewshot_split == self.config.test_split else num_fewshot

fewshotex = self.sample(n_samples)

selected_docs = [x for x in fewshotex if x != doc][:num_fewshot]

if fewshot_as_multiturn:

for doc in selected_docs:

doc_content = self.doc_to_text(doc)

doc_target = self.doc_to_target(doc)

chat_history.append({'role': 'user', 'content': doc_content if self.config.doc_to_choice is None or isinstance(doc_content, str) else self.doc_to_choice(doc)[doc_content]})

chat_history.append({'role': 'assistant', 'content': str(doc_target[0]) if isinstance(doc_target, list) else doc_target if self.config.doc_to_choice is None or isinstance(doc_target, str) else str(self.doc_to_choice(doc)[doc_target])})

else:

chat_history.append({'role': 'user', 'content': self.get_context(doc, num_fewshot)})

return chat_history

def sample(self, n):

return self.rnd.sample(self.docs, n)

class FirstNSampler(ContextSampler):

def sample(self, n) -> None:

assert n <= len(self.docs), f'Error: number of fewshot samples requested exceeds the {len(self.docs)} that are available.'

return self.docs[:n]

class BalancedSampler(ContextSampler):

def sample(self, n) -> None:

pass

class ManualSampler(ContextSampler):

def sample(self, n) -> None:

""""""

pass

SAMPLER_REGISTRY = {'default': ContextSampler, 'first_n': FirstNSampler}

def get_sampler(name):

try:

return SAMPLER_REGISTRY[name]

except KeyError: