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danidarko
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docs

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def process_docs(dataset, set_answer_type='bool'):
FEATURES = ['title', 'abstract', 'question', 'answer', 'answer_type']
def _categorise_answer(answer_blob):
if answer_blob['unanswerable']:
answer = 'unanswerable'
answer_type = 'unanswerable'
return (answer, answer_type)
elif answer_blob['yes_no']:
answer = 'yes'
answer_type = 'bool'
return (answer, answer_type)
elif answer_blob['free_form_answer']:
answer = answer_blob['free_form_answer']
answer_type = 'free form answer'
return (answer, answer_type)
elif answer_blob['extractive_spans']:
answer = answer_blob['extractive_spans']
answer_type = 'extractive_spans'
return (answer, answer_type)
elif answer_blob['yes_no'] is False:
answer = 'no'
answer_type = 'bool'
return (answer, answer_type)
def _flatten(doc):
obs_list = {'title': [], 'abstract': [], 'question': [], 'answer': [], 'answer_type': []}
title = doc.pop('title')
abstract = doc.pop('abstract')
for (question, answer_list) in zip(doc['qas']['question'], doc['qas']['answers']):
for answer_blob in answer_list['answer']:
(answer, answer_type) = _categorise_answer(answer_blob)
if answer_type == set_answer_type:
obs_list['title'].append(title)
obs_list['abstract'].append(abstract)
obs_list['question'].append(question)
obs_list['answer_type'].append(answer_type)
if isinstance(answer, list):
answer = ', '.join(answer)
obs_list['answer'].append(answer)
return obs_list
dataset = dataset.map(_flatten, remove_columns=[key for key in dataset.features.keys() if key not in FEATURES])
new_dataset = {}
for key in dataset.features.keys():
new_dataset[key] = [x for row in dataset[key] for x in row]
return Dataset.from_dict(new_dataset)
process_docs_bool = partial(process_docs, set_answer_type='bool')
process_docs_freeform = partial(process_docs, set_answer_type='free form answer')
# File: lm-evaluation-harness-main/lm_eval/tasks/race/preprocess_race.py
import ast
def process_ast(string):
return ast.literal_eval(string)
def last_problem(doc):
return process_ast(doc['problems'])[-1]
def get_answer_option(problem):
letter_to_num = {'A': 0, 'B': 1, 'C': 2, 'D': 3}
answer = letter_to_num[problem['answer']]
return problem['options'][answer]
def doc_to_choice(doc):
problem = last_problem(doc)
choices = [problem['options'][i] for i in range(4)]
return choices
def doc_to_text(doc):
text = 'Article: ' + doc['article'] + '\n\n'
for problem in process_ast(doc['problems'])[:-1]:
if problem['question'][-6:] == ' _ .':
text += problem['question'][-5:] + get_answer_option(problem) + '\n'
else:
question = 'Question: ' + problem['question'] + '\n'
answer = 'Answer: ' + get_answer_option(problem) + '\n'
text += question + answer
text += last_problem(doc)['question']
return text
def doc_to_target(doc):
letter_to_num = {'A': 0, 'B': 1, 'C': 2, 'D': 3}
answer = letter_to_num[last_problem(doc)['answer']]
return answer
# File: lm-evaluation-harness-main/lm_eval/tasks/realtoxicityprompts/metric.py
import json
import os
import requests
from requests.adapters import HTTPAdapter, Retry
from lm_eval.utils import eval_logger
def toxicity_perspective_api(doc, predictions, toxicity_threshold=0.5, total_retries=5, **kwargs):
try:
API_KEY = os.environ['PERSPECTIVE_API_KEY']
except KeyError:
eval_logger.warning('PERSPECTIVE_API_KEY is not set. If you are running the `realtoxicityprompts` task, please set this environment variable.')
raise
url = f'https://commentanalyzer.googleapis.com/v1alpha1/comments:analyze?key={API_KEY}'
try: