danidarko/docs · Datasets at Hugging Face

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except Exception as e:
eval_logger.warning('Could not gate the repository')
eval_logger.info(repr(e))
self.api.upload_folder(repo_id=repo_id, folder_path=str(path), path_in_repo=self.general_config_tracker.model_name_sanitized, repo_type='dataset', commit_message=f'Adding samples results for {task_name} to {self.general_config_tracker.model_name}')
eval_logger.info(f'Successfully pushed sample results for task: {task_name} to the Hugging Face Hub. You can find them at: {repo_id}')
except Exception as e:
eval_logger.warning('Could not save sample results')
eval_logger.info(repr(e))
else:
eval_logger.info('Output path not provided, skipping saving sample results')

def recreate_metadata_card(self) -> None:
eval_logger.info('Recreating metadata card')
repo_id = self.details_repo if self.public_repo else self.details_repo_private
files_in_repo = self.api.list_repo_files(repo_id=repo_id, repo_type='dataset')
results_files = get_results_filenames(files_in_repo)
sample_files = get_sample_results_filenames(files_in_repo)
latest_task_results_datetime = defaultdict(lambda : datetime.min.isoformat())
for file_path in sample_files:
file_path = Path(file_path)
filename = file_path.name
model_name = file_path.parent
task_name = get_file_task_name(filename)
results_datetime = get_file_datetime(filename)
task_name_sanitized = sanitize_task_name(task_name)
samples_key = f'{model_name}__{task_name_sanitized}'
results_key = f'{model_name}__results'
latest_datetime = max(latest_task_results_datetime[samples_key], results_datetime)
latest_task_results_datetime[samples_key] = latest_datetime
latest_task_results_datetime[results_key] = max(latest_task_results_datetime[results_key], latest_datetime)
card_metadata = MetadataConfigs()
for file_path in results_files:
file_path = Path(file_path)
results_filename = file_path.name
model_name = file_path.parent
eval_date = get_file_datetime(results_filename)
eval_date_sanitized = re.sub('[^\\w\\.]', '_', eval_date)
results_filename = Path('**') / Path(results_filename).name
config_name = f'{model_name}__results'
sanitized_last_eval_date_results = re.sub('[^\\w\\.]', '_', latest_task_results_datetime[config_name])
if eval_date_sanitized == sanitized_last_eval_date_results:
current_results = card_metadata.get(config_name, {'data_files': []})
current_results['data_files'].append({'split': eval_date_sanitized, 'path': [str(results_filename)]})
card_metadata[config_name] = current_results
card_metadata[config_name]['data_files'].append({'split': 'latest', 'path': [str(results_filename)]})
for file_path in sample_files:
file_path = Path(file_path)
filename = file_path.name
model_name = file_path.parent
task_name = get_file_task_name(filename)
eval_date = get_file_datetime(filename)
task_name_sanitized = sanitize_task_name(task_name)
eval_date_sanitized = re.sub('[^\\w\\.]', '_', eval_date)
results_filename = Path('**') / Path(filename).name
config_name = f'{model_name}__{task_name_sanitized}'
sanitized_last_eval_date_results = re.sub('[^\\w\\.]', '_', latest_task_results_datetime[config_name])
if eval_date_sanitized == sanitized_last_eval_date_results:
current_details_for_task = card_metadata.get(config_name, {'data_files': []})
current_details_for_task['data_files'].append({'split': eval_date_sanitized, 'path': [str(results_filename)]})
card_metadata[config_name] = current_details_for_task
card_metadata[config_name]['data_files'].append({'split': 'latest', 'path': [str(results_filename)]})
latest_datetime = max(latest_task_results_datetime.values())
latest_model_name = max(latest_task_results_datetime, key=lambda k: latest_task_results_datetime[k])
last_results_file = [f for f in results_files if latest_datetime.replace(':', '-') in f][0]
last_results_file_path = hf_hub_url(repo_id=repo_id, filename=last_results_file, repo_type='dataset')
latest_results_file = load_dataset('json', data_files=last_results_file_path, split='train')
results_dict = latest_results_file['results'][0]
new_dictionary = {'all': results_dict}
new_dictionary.update(results_dict)
results_string = json.dumps(new_dictionary, indent=4)
dataset_summary = 'Dataset automatically created during the evaluation run of model '
if self.general_config_tracker.model_source == 'hf':
dataset_summary += f'[{self.general_config_tracker.model_name}](https://huggingface.co/{self.general_config_tracker.model_name})\n'
else:
dataset_summary += f'{self.general_config_tracker.model_name}\n'
dataset_summary += f'The dataset is composed of {len(card_metadata) - 1} configuration(s), each one corresponding to one of the evaluated task.\n\nThe dataset has been created from {len(results_files)} run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.\n\nAn additional configuration "results" store all the aggregated results of the run.\n\nTo load the details from a run, you can for instance do the following:\n'
if self.general_config_tracker.model_source == 'hf':
dataset_summary += f'```python\nfrom datasets import load_dataset\ndata = load_dataset(\n\t"{repo_id}",\n\tname="{latest_model_name}",\n\tsplit="latest"\n)\n```\n\n'
dataset_summary += f"""## Latest results\n\nThese are the [latest results from run {latest_datetime}]({last_results_file_path.replace('/resolve/', '/blob/')}) (note that there might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):\n\n```python\n{results_string}\n```"""
card_data = DatasetCardData(dataset_summary=dataset_summary, repo_url=f'https://huggingface.co/{self.general_config_tracker.model_name}', pretty_name=f'Evaluation run of {self.general_config_tracker.model_name}', leaderboard_url=self.leaderboard_url, point_of_contact=self.point_of_contact)
card_metadata.to_dataset_card_data(card_data)
card = DatasetCard.from_template(card_data, pretty_name=card_data.pretty_name)
card.push_to_hub(repo_id, repo_type='dataset')

# File: lm-evaluation-harness-main/lm_eval/loggers/utils.py
import logging
import os
import re
import subprocess
from pathlib import Path
from typing import Any, Dict, Optional, Tuple, Union
import numpy as np
from torch.utils.collect_env import get_pretty_env_info
from transformers import __version__ as trans_version
logger = logging.getLogger(__name__)

def remove_none_pattern(input_string: str) -> Tuple[str, bool]:
pattern = re.compile(',none$')
result = re.sub(pattern, '', input_string)
removed = result != input_string

except Exception as e:

eval_logger.warning('Could not gate the repository')

eval_logger.info(repr(e))

self.api.upload_folder(repo_id=repo_id, folder_path=str(path), path_in_repo=self.general_config_tracker.model_name_sanitized, repo_type='dataset', commit_message=f'Adding samples results for {task_name} to {self.general_config_tracker.model_name}')

eval_logger.info(f'Successfully pushed sample results for task: {task_name} to the Hugging Face Hub. You can find them at: {repo_id}')

except Exception as e:

eval_logger.warning('Could not save sample results')

eval_logger.info(repr(e))

else:

eval_logger.info('Output path not provided, skipping saving sample results')

def recreate_metadata_card(self) -> None:

eval_logger.info('Recreating metadata card')

repo_id = self.details_repo if self.public_repo else self.details_repo_private

files_in_repo = self.api.list_repo_files(repo_id=repo_id, repo_type='dataset')

results_files = get_results_filenames(files_in_repo)

sample_files = get_sample_results_filenames(files_in_repo)

latest_task_results_datetime = defaultdict(lambda : datetime.min.isoformat())

for file_path in sample_files:

file_path = Path(file_path)

filename = file_path.name

model_name = file_path.parent

task_name = get_file_task_name(filename)

results_datetime = get_file_datetime(filename)

task_name_sanitized = sanitize_task_name(task_name)

samples_key = f'{model_name}__{task_name_sanitized}'

results_key = f'{model_name}__results'

latest_datetime = max(latest_task_results_datetime[samples_key], results_datetime)

latest_task_results_datetime[samples_key] = latest_datetime

latest_task_results_datetime[results_key] = max(latest_task_results_datetime[results_key], latest_datetime)

card_metadata = MetadataConfigs()

for file_path in results_files:

file_path = Path(file_path)

results_filename = file_path.name

model_name = file_path.parent

eval_date = get_file_datetime(results_filename)

eval_date_sanitized = re.sub('[^\\w\\.]', '_', eval_date)

results_filename = Path('**') / Path(results_filename).name

config_name = f'{model_name}__results'

sanitized_last_eval_date_results = re.sub('[^\\w\\.]', '_', latest_task_results_datetime[config_name])

if eval_date_sanitized == sanitized_last_eval_date_results:

current_results = card_metadata.get(config_name, {'data_files': []})

current_results['data_files'].append({'split': eval_date_sanitized, 'path': [str(results_filename)]})

card_metadata[config_name] = current_results

card_metadata[config_name]['data_files'].append({'split': 'latest', 'path': [str(results_filename)]})

for file_path in sample_files:

file_path = Path(file_path)

filename = file_path.name

model_name = file_path.parent

task_name = get_file_task_name(filename)

eval_date = get_file_datetime(filename)

task_name_sanitized = sanitize_task_name(task_name)

eval_date_sanitized = re.sub('[^\\w\\.]', '_', eval_date)

results_filename = Path('**') / Path(filename).name

config_name = f'{model_name}__{task_name_sanitized}'

sanitized_last_eval_date_results = re.sub('[^\\w\\.]', '_', latest_task_results_datetime[config_name])

if eval_date_sanitized == sanitized_last_eval_date_results:

current_details_for_task = card_metadata.get(config_name, {'data_files': []})

current_details_for_task['data_files'].append({'split': eval_date_sanitized, 'path': [str(results_filename)]})

card_metadata[config_name] = current_details_for_task

card_metadata[config_name]['data_files'].append({'split': 'latest', 'path': [str(results_filename)]})

latest_datetime = max(latest_task_results_datetime.values())

latest_model_name = max(latest_task_results_datetime, key=lambda k: latest_task_results_datetime[k])

last_results_file = [f for f in results_files if latest_datetime.replace(':', '-') in f][0]

last_results_file_path = hf_hub_url(repo_id=repo_id, filename=last_results_file, repo_type='dataset')

latest_results_file = load_dataset('json', data_files=last_results_file_path, split='train')

results_dict = latest_results_file['results'][0]

new_dictionary = {'all': results_dict}

new_dictionary.update(results_dict)

results_string = json.dumps(new_dictionary, indent=4)

dataset_summary = 'Dataset automatically created during the evaluation run of model '

if self.general_config_tracker.model_source == 'hf':

dataset_summary += f'[{self.general_config_tracker.model_name}](https://huggingface.co/{self.general_config_tracker.model_name})\n'

else:

dataset_summary += f'{self.general_config_tracker.model_name}\n'

dataset_summary += f'The dataset is composed of {len(card_metadata) - 1} configuration(s), each one corresponding to one of the evaluated task.\n\nThe dataset has been created from {len(results_files)} run(s). Each run can be found as a specific split in each configuration, the split being named using the timestamp of the run.The "train" split is always pointing to the latest results.\n\nAn additional configuration "results" store all the aggregated results of the run.\n\nTo load the details from a run, you can for instance do the following:\n'

if self.general_config_tracker.model_source == 'hf':

dataset_summary += f'```python\nfrom datasets import load_dataset\ndata = load_dataset(\n\t"{repo_id}",\n\tname="{latest_model_name}",\n\tsplit="latest"\n)\n```\n\n'

dataset_summary += f"""## Latest results\n\nThese are the [latest results from run {latest_datetime}]({last_results_file_path.replace('/resolve/', '/blob/')}) (note that there might be results for other tasks in the repos if successive evals didn't cover the same tasks. You find each in the results and the "latest" split for each eval):\n\n```python\n{results_string}\n```"""

card_data = DatasetCardData(dataset_summary=dataset_summary, repo_url=f'https://huggingface.co/{self.general_config_tracker.model_name}', pretty_name=f'Evaluation run of {self.general_config_tracker.model_name}', leaderboard_url=self.leaderboard_url, point_of_contact=self.point_of_contact)

card_metadata.to_dataset_card_data(card_data)

card = DatasetCard.from_template(card_data, pretty_name=card_data.pretty_name)

card.push_to_hub(repo_id, repo_type='dataset')

# File: lm-evaluation-harness-main/lm_eval/loggers/utils.py

import logging

import os

import re

import subprocess

from pathlib import Path

from typing import Any, Dict, Optional, Tuple, Union

import numpy as np

from torch.utils.collect_env import get_pretty_env_info

from transformers import __version__ as trans_version

logger = logging.getLogger(__name__)

def remove_none_pattern(input_string: str) -> Tuple[str, bool]:

pattern = re.compile(',none$')

result = re.sub(pattern, '', input_string)

removed = result != input_string