code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
values |
|---|---|---|---|---|---|---|---|
def __init__(
self,
cfg=None,
):
"""
Initialization method.
:param cfg: configuration of sandbox.
"""
self.cfg = cfg
self.watcher = SandBoxWatcher(self.cfg)
self.watcher.watch_cfgs([(cfg, 'sandbox')])
# jobs to probe, refine_recipe,... |
Initialization method.
:param cfg: configuration of sandbox.
| __init__ | python | modelscope/data-juicer | data_juicer/core/sandbox/pipelines.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/core/sandbox/pipelines.py | Apache-2.0 |
def run(self):
"""
Running the sandbox pipeline at once or in HPO style.
"""
if self.cfg.hpo_config is not None:
# execute_hpo_wandb contains running one_trail with HPO scheduler
self.execute_hpo_wandb()
else:
self.one_trial() |
Running the sandbox pipeline at once or in HPO style.
| run | python | modelscope/data-juicer | data_juicer/core/sandbox/pipelines.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/core/sandbox/pipelines.py | Apache-2.0 |
def one_trial(self):
"""
Running the sandbox pipeline at once.
Users can flexibly conduct some steps of the whole sandbox pipeline
according to their own need and configuration. The watcher will
automatically track the results in terms of data, model and specified
... |
Running the sandbox pipeline at once.
Users can flexibly conduct some steps of the whole sandbox pipeline
according to their own need and configuration. The watcher will
automatically track the results in terms of data, model and specified
evaluation metrics to the watche... | one_trial | python | modelscope/data-juicer | data_juicer/core/sandbox/pipelines.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/core/sandbox/pipelines.py | Apache-2.0 |
def execute_hpo_wandb(self):
"""
Running the sandbox pipeline in HPO style.
Users can flexibly conduct some steps of the whole sandbox pipeline
according to their own need and configuration. The watcher will
automatically track the results in terms of data, model and specifi... |
Running the sandbox pipeline in HPO style.
Users can flexibly conduct some steps of the whole sandbox pipeline
according to their own need and configuration. The watcher will
automatically track the results in terms of data, model and specified
evaluation metrics to the w... | execute_hpo_wandb | python | modelscope/data-juicer | data_juicer/core/sandbox/pipelines.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/core/sandbox/pipelines.py | Apache-2.0 |
def _tex_proj_loader(self, file_or_dir_path):
r"""function to load the tex files from a tar file or a gzip file. The
function will return a tuple containing a list of tex files and the
timestamp of the project.
@param file_or_dir_path: path to the tar file or the gzip file
@ret... | function to load the tex files from a tar file or a gzip file. The
function will return a tuple containing a list of tex files and the
timestamp of the project.
@param file_or_dir_path: path to the tar file or the gzip file
@return: tuple containing a list of tex files and the timestam... | _tex_proj_loader | python | modelscope/data-juicer | data_juicer/download/arxiv.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/download/arxiv.py | Apache-2.0 |
def _format_arxiv_id(self, arxiv_id):
r"""this function brings the raw arxiv-id into a format compliant with the
specification from arxiv. This is used to create the url to the arxiv
abstract page.
- Format prior to March 2007:
<archive>/YYMMNNN where N is a 3-digit number
... | this function brings the raw arxiv-id into a format compliant with the
specification from arxiv. This is used to create the url to the arxiv
abstract page.
- Format prior to March 2007:
<archive>/YYMMNNN where N is a 3-digit number
- Format after March 2007: <archive>/YYMM.N... | _format_arxiv_id | python | modelscope/data-juicer | data_juicer/download/arxiv.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/download/arxiv.py | Apache-2.0 |
def _clean_tex_file(self, file_content, arg_macros, non_arg_macros):
r"""function takes a tex file as input and returns a cleaned version. The
cleaned version is a concatenation of the tex files with the
following modifications:
- remove all comments (i.e. all lines starting with %)
... | function takes a tex file as input and returns a cleaned version. The
cleaned version is a concatenation of the tex files with the
following modifications:
- remove all comments (i.e. all lines starting with %)
- remove everything before the first section-like header
- remove e... | _clean_tex_file | python | modelscope/data-juicer | data_juicer/download/arxiv.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/download/arxiv.py | Apache-2.0 |
def _build_non_arg_macros_dict(self, file_content):
r"""function takes the content of a tex file and returns a dictionary
that contains the definitions of all macros that do not use arguments.
The dictionary is of the form {macro_name: macro_value}.
@param file_content: the content of t... | function takes the content of a tex file and returns a dictionary
that contains the definitions of all macros that do not use arguments.
The dictionary is of the form {macro_name: macro_value}.
@param file_content: the content of the tex file as a string.
@return: dict
| _build_non_arg_macros_dict | python | modelscope/data-juicer | data_juicer/download/arxiv.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/download/arxiv.py | Apache-2.0 |
def get_wikipedia_urls(
language='en',
wikidumps_index_prefix='https://dumps.wikimedia.org',
dump_date: Optional[str] = None,
) -> List[str]:
"""
Retrieves all urls pointing to the latest Wikipedia dumps
Args:
language: Desired language of the Wikipedia dump.
wikidumps_index_pre... |
Retrieves all urls pointing to the latest Wikipedia dumps
Args:
language: Desired language of the Wikipedia dump.
wikidumps_index_prefix: The base url for all wikipedia dumps
dump_date: A string formatted as "YYYYMMDD" for the wikipedia dump to use.
If None, latest dump is us... | get_wikipedia_urls | python | modelscope/data-juicer | data_juicer/download/downloader.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/download/downloader.py | Apache-2.0 |
def validate_snapshot_format(snapshot: Optional[str]) -> None:
"""
Validate snapshot format 'YYYY-WW'.
Args:
snapshot: Snapshot string in format 'YYYY-WW' or None
Raises:
ValueError: If format is invalid
"""
if snapshot is None:
return
# Check basic format with reg... |
Validate snapshot format 'YYYY-WW'.
Args:
snapshot: Snapshot string in format 'YYYY-WW' or None
Raises:
ValueError: If format is invalid
| validate_snapshot_format | python | modelscope/data-juicer | data_juicer/download/downloader.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/download/downloader.py | Apache-2.0 |
def __init__(self, dataset_path, suffixes=None, **kwargs):
"""
Initialization method.
:param dataset_path: a dataset file or a dataset directory
:param suffixes: files with specified suffixes to be processed
:param kwargs: extra args
"""
super().__init__(
... |
Initialization method.
:param dataset_path: a dataset file or a dataset directory
:param suffixes: files with specified suffixes to be processed
:param kwargs: extra args
| __init__ | python | modelscope/data-juicer | data_juicer/format/csv_formatter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/format/csv_formatter.py | Apache-2.0 |
def __init__(self, length, feature_keys: List[str] = [], *args, **kwargs):
"""
Initialization method.
:param length: The empty dataset length.
:param feature_keys: feature key name list.
"""
self.length = length
self.feature_keys = feature_keys
if isinsta... |
Initialization method.
:param length: The empty dataset length.
:param feature_keys: feature key name list.
| __init__ | python | modelscope/data-juicer | data_juicer/format/empty_formatter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/format/empty_formatter.py | Apache-2.0 |
def __init__(
self,
dataset_path: str,
type: str,
suffixes: Union[str, List[str], None] = None,
text_keys: List[str] = None,
add_suffix=False,
**kwargs,
):
"""
Initialization method.
:param dataset_path: path to a dataset file or a dat... |
Initialization method.
:param dataset_path: path to a dataset file or a dataset
directory
:param type: a packaged dataset module type (json, csv, etc.)
:param suffixes: files with specified suffixes to be processed
:param text_keys: key names of field that stores sa... | __init__ | python | modelscope/data-juicer | data_juicer/format/formatter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/format/formatter.py | Apache-2.0 |
def load_dataset(self, num_proc: int = 1, global_cfg=None) -> Dataset:
"""
Load a dataset from dataset file or dataset directory, and unify its
format.
:param num_proc: number of processes when loading the dataset
:param global_cfg: global cfg used in consequent processes,
... |
Load a dataset from dataset file or dataset directory, and unify its
format.
:param num_proc: number of processes when loading the dataset
:param global_cfg: global cfg used in consequent processes,
:return: formatted dataset
| load_dataset | python | modelscope/data-juicer | data_juicer/format/formatter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/format/formatter.py | Apache-2.0 |
def __init__(self,
dataset_path: str,
text_keys: List[str] = None,
**kwargs):
"""
Initialization method.
:param dataset_path: a dataset file or a dataset directory
:param text_keys: key names of field that stores sample
text... |
Initialization method.
:param dataset_path: a dataset file or a dataset directory
:param text_keys: key names of field that stores sample
text.
:param kwargs: extra args
| __init__ | python | modelscope/data-juicer | data_juicer/format/formatter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/format/formatter.py | Apache-2.0 |
def load_dataset(self, num_proc: int = 1, global_cfg=None) -> Dataset:
"""
Load a dataset from HuggingFace, and unify its format.
:param num_proc: number of processes when loading the dataset
:param global_cfg: the global cfg used in consequent processes,
:return: formatted data... |
Load a dataset from HuggingFace, and unify its format.
:param num_proc: number of processes when loading the dataset
:param global_cfg: the global cfg used in consequent processes,
:return: formatted dataset
| load_dataset | python | modelscope/data-juicer | data_juicer/format/formatter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/format/formatter.py | Apache-2.0 |
def add_suffixes(datasets: DatasetDict, num_proc: int = 1) -> Dataset:
"""
Add suffix filed to datasets.
:param datasets: a DatasetDict object
:param num_proc: number of processes to add suffixes
:return: datasets with suffix features.
"""
logger.info('Add suffix column for dataset')
fr... |
Add suffix filed to datasets.
:param datasets: a DatasetDict object
:param num_proc: number of processes to add suffixes
:return: datasets with suffix features.
| add_suffixes | python | modelscope/data-juicer | data_juicer/format/formatter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/format/formatter.py | Apache-2.0 |
def unify_format(
dataset: Dataset,
text_keys: Union[List[str], str] = 'text',
num_proc: int = 1,
global_cfg=None,
) -> Dataset:
"""
Get an unified internal format, conduct the following modifications.
1. check keys of dataset
2. filter out those samples with empty or None text
:p... |
Get an unified internal format, conduct the following modifications.
1. check keys of dataset
2. filter out those samples with empty or None text
:param dataset: input dataset
:param text_keys: original text key(s) of dataset.
:param num_proc: number of processes for mapping
:param globa... | unify_format | python | modelscope/data-juicer | data_juicer/format/formatter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/format/formatter.py | Apache-2.0 |
def load_formatter(dataset_path,
text_keys=None,
suffixes=None,
add_suffix=False,
**kwargs) -> BaseFormatter:
"""
Load the appropriate formatter for different types of data formats.
:param dataset_path: Path to dataset file or data... |
Load the appropriate formatter for different types of data formats.
:param dataset_path: Path to dataset file or dataset directory
:param text_keys: key names of field that stores sample text.
Default: None
:param suffixes: the suffix of files that will be read.
Default: None
:para... | load_formatter | python | modelscope/data-juicer | data_juicer/format/load.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/format/load.py | Apache-2.0 |
def extract_txt_from_docx(fn, tgt_path):
"""
Extract text from a docx file and save to target path.
:param fn: path to input pdf file
:param tgt_path: path to save text file.
"""
doc = Document(fn)
text = [para.text for para in doc.paragraphs if para.text.strip()]
base_fn = os.path.base... |
Extract text from a docx file and save to target path.
:param fn: path to input pdf file
:param tgt_path: path to save text file.
| extract_txt_from_docx | python | modelscope/data-juicer | data_juicer/format/text_formatter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/format/text_formatter.py | Apache-2.0 |
def extract_txt_from_pdf(fn, tgt_path):
"""
Extract text from a pdf file and save to target path.
:param fn: path to input pdf file
:param tgt_path: path to save text file.
"""
with pdfplumber.open(fn) as pdf:
text = []
for page in pdf.pages:
# remove tables from eac... |
Extract text from a pdf file and save to target path.
:param fn: path to input pdf file
:param tgt_path: path to save text file.
| extract_txt_from_pdf | python | modelscope/data-juicer | data_juicer/format/text_formatter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/format/text_formatter.py | Apache-2.0 |
def __init__(self,
dataset_path,
suffixes=None,
add_suffix=False,
**kwargs):
"""
Initialization method.
:param dataset_path: a dataset file or a dataset directory
:param suffixes: files with specified suffixes to be pro... |
Initialization method.
:param dataset_path: a dataset file or a dataset directory
:param suffixes: files with specified suffixes to be processed
:param add_suffix: Whether to add file suffix to dataset meta
info
:param kwargs: extra args
| __init__ | python | modelscope/data-juicer | data_juicer/format/text_formatter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/format/text_formatter.py | Apache-2.0 |
def load_dataset(self, num_proc: int = 1, global_cfg=None) -> Dataset:
"""
Load a dataset from local text-type files.
:param num_proc: number of processes when loading the dataset
:param global_cfg: the global cfg used in consequent processes,
:return: unified_format_dataset.
... |
Load a dataset from local text-type files.
:param num_proc: number of processes when loading the dataset
:param global_cfg: the global cfg used in consequent processes,
:return: unified_format_dataset.
| load_dataset | python | modelscope/data-juicer | data_juicer/format/text_formatter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/format/text_formatter.py | Apache-2.0 |
def catch_map_batches_exception(method, skip_op_error=False, op_name=None):
"""
For batched-map sample-level fault tolerance.
"""
if op_name is None:
op_name = method.__name__
@wraps(method)
@convert_arrow_to_python
def wrapper(samples, *args, **kwargs):
try:
re... |
For batched-map sample-level fault tolerance.
| catch_map_batches_exception | python | modelscope/data-juicer | data_juicer/ops/base_op.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/base_op.py | Apache-2.0 |
def catch_map_single_exception(method,
return_sample=True,
skip_op_error=False,
op_name=None):
"""
For single-map sample-level fault tolerance.
The input sample is expected batch_size = 1.
"""
if op_name is... |
For single-map sample-level fault tolerance.
The input sample is expected batch_size = 1.
| catch_map_single_exception | python | modelscope/data-juicer | data_juicer/ops/base_op.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/base_op.py | Apache-2.0 |
def __init__(self, *args, **kwargs):
"""
Base class of operators.
:param text_key: the key name of field that stores sample texts
to be processed.
:param image_key: the key name of field that stores sample image list
to be processed
:param audio_key: the ... |
Base class of operators.
:param text_key: the key name of field that stores sample texts
to be processed.
:param image_key: the key name of field that stores sample image list
to be processed
:param audio_key: the key name of field that stores sample audio list
... | __init__ | python | modelscope/data-juicer | data_juicer/ops/base_op.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/base_op.py | Apache-2.0 |
def remove_extra_parameters(self, param_dict, keys=None):
"""
at the beginning of the init of the mapper op, call
self.remove_extra_parameters(locals())
to get the init parameter dict of the op for convenience
"""
if keys is None:
param_dict = {
... |
at the beginning of the init of the mapper op, call
self.remove_extra_parameters(locals())
to get the init parameter dict of the op for convenience
| remove_extra_parameters | python | modelscope/data-juicer | data_juicer/ops/base_op.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/base_op.py | Apache-2.0 |
def add_parameters(self, init_parameter_dict, **extra_param_dict):
"""
add parameters for each sample, need to keep extra_param_dict
and init_parameter_dict unchanged.
"""
related_parameters = copy.deepcopy(init_parameter_dict)
related_parameters.update(extra_para... |
add parameters for each sample, need to keep extra_param_dict
and init_parameter_dict unchanged.
| add_parameters | python | modelscope/data-juicer | data_juicer/ops/base_op.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/base_op.py | Apache-2.0 |
def __init__(self, *args, **kwargs):
"""
Base class that conducts data editing.
:param text_key: the key name of field that stores sample texts
to be processed.
:param image_key: the key name of field that stores sample image list
to be processed
:param a... |
Base class that conducts data editing.
:param text_key: the key name of field that stores sample texts
to be processed.
:param image_key: the key name of field that stores sample image list
to be processed
:param audio_key: the key name of field that stores samp... | __init__ | python | modelscope/data-juicer | data_juicer/ops/base_op.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/base_op.py | Apache-2.0 |
def __init__(self, *args, **kwargs):
"""
Base class that removes specific info.
:param text_key: the key name of field that stores sample texts
to be processed
:param image_key: the key name of field that stores sample image list
to be processed
:param au... |
Base class that removes specific info.
:param text_key: the key name of field that stores sample texts
to be processed
:param image_key: the key name of field that stores sample image list
to be processed
:param audio_key: the key name of field that stores sampl... | __init__ | python | modelscope/data-juicer | data_juicer/ops/base_op.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/base_op.py | Apache-2.0 |
def __init__(self, *args, **kwargs):
"""
Base class that conducts deduplication.
:param text_key: the key name of field that stores sample texts
to be processed
:param image_key: the key name of field that stores sample image list
to be processed
:param a... |
Base class that conducts deduplication.
:param text_key: the key name of field that stores sample texts
to be processed
:param image_key: the key name of field that stores sample image list
to be processed
:param audio_key: the key name of field that stores samp... | __init__ | python | modelscope/data-juicer | data_juicer/ops/base_op.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/base_op.py | Apache-2.0 |
def __init__(self, *args, **kwargs):
"""
Base class that group samples.
:param text_key: the key name of field that stores sample texts
to be processed
:param image_key: the key name of field that stores sample image list
to be processed
:param audio_key:... |
Base class that group samples.
:param text_key: the key name of field that stores sample texts
to be processed
:param image_key: the key name of field that stores sample image list
to be processed
:param audio_key: the key name of field that stores sample audio ... | __init__ | python | modelscope/data-juicer | data_juicer/ops/base_op.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/base_op.py | Apache-2.0 |
def load_ops(process_list):
"""
Load op list according to the process list from config file.
:param process_list: A process list. Each item is an op name and its
arguments.
:return: The op instance list.
"""
ops = []
new_process_list = []
for process in process_list:
op... |
Load op list according to the process list from config file.
:param process_list: A process list. Each item is an op name and its
arguments.
:return: The op instance list.
| load_ops | python | modelscope/data-juicer | data_juicer/ops/load.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/load.py | Apache-2.0 |
def register_event_handler(self, event_type: str, handler: Callable):
"""Register a handler for a specific event type.
Args:
event_type: Type of event to handle
handler: Callback function to handle the event
"""
if event_type not in self.event_handlers:
... | Register a handler for a specific event type.
Args:
event_type: Type of event to handle
handler: Callback function to handle the event
| register_event_handler | python | modelscope/data-juicer | data_juicer/ops/mixins.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/mixins.py | Apache-2.0 |
def trigger_event(self, event_type: str, data: Dict):
"""Trigger an event and call all registered handlers.
Args:
event_type: Type of event to trigger
data: Event data to pass to handlers
"""
if event_type in self.event_handlers:
for handler in self.e... | Trigger an event and call all registered handlers.
Args:
event_type: Type of event to trigger
data: Event data to pass to handlers
| trigger_event | python | modelscope/data-juicer | data_juicer/ops/mixins.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/mixins.py | Apache-2.0 |
def start_polling(self,
event_type: str,
poll_func: Callable,
interval: int = 60):
"""Start polling for a specific event type.
Args:
event_type: Type of event to poll for
poll_func: Function to call for polling
... | Start polling for a specific event type.
Args:
event_type: Type of event to poll for
poll_func: Function to call for polling
interval: Polling interval in seconds
| start_polling | python | modelscope/data-juicer | data_juicer/ops/mixins.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/mixins.py | Apache-2.0 |
def stop_polling(self, event_type: str):
"""Stop polling for a specific event type.
Args:
event_type: Type of event to stop polling for
"""
if event_type in self.polling_threads:
self.stop_polling_flags[event_type] = True
self.polling_threads[event_ty... | Stop polling for a specific event type.
Args:
event_type: Type of event to stop polling for
| stop_polling | python | modelscope/data-juicer | data_juicer/ops/mixins.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/mixins.py | Apache-2.0 |
def wait_for_completion(self,
condition_func: Callable[[], bool],
timeout: int = 3600,
poll_interval: int = 10,
error_message: str = 'Operation timed out'):
"""Wait for a condition to be met.
... | Wait for a condition to be met.
Args:
condition_func: Function that returns True when condition is met
timeout: Maximum time to wait in seconds
poll_interval: Polling interval in seconds
error_message: Error message to raise on timeout
Raises:
... | wait_for_completion | python | modelscope/data-juicer | data_juicer/ops/mixins.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/mixins.py | Apache-2.0 |
def send_notification(self,
message: str,
notification_type: str = None,
**kwargs):
"""Send a notification message.
Args:
message: The message to send
notification_type: The type of notification to sen... | Send a notification message.
Args:
message: The message to send
notification_type: The type of notification to send.
Email, Slack, DingTalk.
If None, send nothing
**kwargs: Additional arguments to pass to the no... | send_notification | python | modelscope/data-juicer | data_juicer/ops/mixins.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/mixins.py | Apache-2.0 |
def _send_email_notification(self, message: str, **kwargs):
"""Send an email notification.
Args:
message: The message to send
**kwargs: Additional parameters for email configuration
(recipients, subject, etc.)
Returns:
bool: Whether the... | Send an email notification.
Args:
message: The message to send
**kwargs: Additional parameters for email configuration
(recipients, subject, etc.)
Returns:
bool: Whether the email was sent successfully
| _send_email_notification | python | modelscope/data-juicer | data_juicer/ops/mixins.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/mixins.py | Apache-2.0 |
def _send_slack_notification(self, message: str, **kwargs):
"""Send a Slack notification.
Args:
message: The message to send
**kwargs: Additional parameters for Slack configuration
(webhook_url, channel, etc.)
Returns:
bool: Whether the... | Send a Slack notification.
Args:
message: The message to send
**kwargs: Additional parameters for Slack configuration
(webhook_url, channel, etc.)
Returns:
bool: Whether the notification was sent successfully
| _send_slack_notification | python | modelscope/data-juicer | data_juicer/ops/mixins.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/mixins.py | Apache-2.0 |
def _send_dingtalk_notification(self, message: str, **kwargs):
"""Send a DingTalk notification.
Args:
message: The message to send
**kwargs: Additional parameters for DingTalk configuration
(access_token, secret, etc.)
Returns:
bool: Wh... | Send a DingTalk notification.
Args:
message: The message to send
**kwargs: Additional parameters for DingTalk configuration
(access_token, secret, etc.)
Returns:
bool: Whether the notification was sent successfully
| _send_dingtalk_notification | python | modelscope/data-juicer | data_juicer/ops/mixins.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/mixins.py | Apache-2.0 |
def fuse_operators(ops, probe_res=None):
"""
Fuse the input ops list and return the fused ops list.
:param ops: the corresponding list of op objects.
:param probe_res: the probed speed for each OP from Monitor.
:return: a list of fused op objects.
"""
if probe_res is None:
probe_res... |
Fuse the input ops list and return the fused ops list.
:param ops: the corresponding list of op objects.
:param probe_res: the probed speed for each OP from Monitor.
:return: a list of fused op objects.
| fuse_operators | python | modelscope/data-juicer | data_juicer/ops/op_fusion.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/op_fusion.py | Apache-2.0 |
def fuse_filter_group(original_filter_group):
"""
Fuse single filter group and return the fused filter group.
:param original_filter_group: the original filter group, including op
definitions and objects.
:return: the fused definitions and objects of the input filter group.
"""
fused_gr... |
Fuse single filter group and return the fused filter group.
:param original_filter_group: the original filter group, including op
definitions and objects.
:return: the fused definitions and objects of the input filter group.
| fuse_filter_group | python | modelscope/data-juicer | data_juicer/ops/op_fusion.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/op_fusion.py | Apache-2.0 |
def __init__(self, name: str, fused_filters: List):
"""
Initialization method.
:param fused_filters: a list of filters to be fused.
"""
self._name = name
super().__init__()
self.fused_filters = fused_filters
# set accelerator to 'cuda' if there exists any... |
Initialization method.
:param fused_filters: a list of filters to be fused.
| __init__ | python | modelscope/data-juicer | data_juicer/ops/op_fusion.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/op_fusion.py | Apache-2.0 |
def __init__(self,
api_model: str = 'gpt-4o',
meta_tag_key: str = MetaKeys.dialog_sentiment_labels,
target_tags: Optional[List[str]] = None,
*,
api_endpoint: Optional[str] = None,
response_path: Optional[str] = None,
... |
Initialization method.
:param api_model: API model name.
:param meta_tag_key: The key of the meta tag to be mapped.
:param target_tags: The tags that is supposed to be mapped to.
:param api_endpoint: URL endpoint for the API.
:param response_path: Path to extract content... | __init__ | python | modelscope/data-juicer | data_juicer/ops/aggregator/meta_tags_aggregator.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/aggregator/meta_tags_aggregator.py | Apache-2.0 |
def strip(document, strip_characters):
"""
Way faster than document.strip(strip_characters) since strip_characters is
now a set instead of a str, and it contains a lot of elements (all the
emojis).
:param document: document to be processed
:param strip_characters: characters used for stripping ... |
Way faster than document.strip(strip_characters) since strip_characters is
now a set instead of a str, and it contains a lot of elements (all the
emojis).
:param document: document to be processed
:param strip_characters: characters used for stripping document
:return: stripped document
| strip | python | modelscope/data-juicer | data_juicer/ops/common/helper_func.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/common/helper_func.py | Apache-2.0 |
def split_on_whitespace(document, new_line=False, tab=False):
"""
This method also removes concatenated spaces.
:param document: document to be split
:param new_line: whether to split document with '\\\\n'
:param tag: whether to split document with '\\\\t'
:return: word list obtained after spli... |
This method also removes concatenated spaces.
:param document: document to be split
:param new_line: whether to split document with '\\n'
:param tag: whether to split document with '\\t'
:return: word list obtained after splitting document
| split_on_whitespace | python | modelscope/data-juicer | data_juicer/ops/common/helper_func.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/common/helper_func.py | Apache-2.0 |
def split_on_newline_tab_whitespace(document):
"""
This method is used to split the document into different levels of sub-
sentences.
First split on "\\\\n", then on "\\\\t", then on " ".
:param document: document to be split
:return: sentence list obtained after splitting document
"""
... |
This method is used to split the document into different levels of sub-
sentences.
First split on "\\n", then on "\\t", then on " ".
:param document: document to be split
:return: sentence list obtained after splitting document
| split_on_newline_tab_whitespace | python | modelscope/data-juicer | data_juicer/ops/common/helper_func.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/common/helper_func.py | Apache-2.0 |
def merge_on_whitespace_tab_newline(sentences):
"""
This method is used to merge different levels of sub-sentences into one
document. Invert the method split_on_newline_tab_whitespace. Removes
concatenated separators.
:param sentences: sentence list to be merged
:return: document obtained after... |
This method is used to merge different levels of sub-sentences into one
document. Invert the method split_on_newline_tab_whitespace. Removes
concatenated separators.
:param sentences: sentence list to be merged
:return: document obtained after merging sub-sentences
| merge_on_whitespace_tab_newline | python | modelscope/data-juicer | data_juicer/ops/common/helper_func.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/common/helper_func.py | Apache-2.0 |
def words_augmentation(words, group_size, join_char):
"""
Augment words, especially for Chinese (without a space between words) and
Vietnamese (with a space between syllables).
:param word: word list to be augmented
:param group_size: the size of word groups that need to be merged
:param join_c... |
Augment words, especially for Chinese (without a space between words) and
Vietnamese (with a space between syllables).
:param word: word list to be augmented
:param group_size: the size of word groups that need to be merged
:param join_char: characters to be added between word group
:return: w... | words_augmentation | python | modelscope/data-juicer | data_juicer/ops/common/helper_func.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/common/helper_func.py | Apache-2.0 |
def get_words_from_document(
document,
token_func=None,
new_line=True,
tab=True,
):
"""
Get words from a document. Useful to compute ratios, like the
stopwords ratio.
:param document: document that need to split words.
:param token_func: function of tokenizer, if specified, the func... |
Get words from a document. Useful to compute ratios, like the
stopwords ratio.
:param document: document that need to split words.
:param token_func: function of tokenizer, if specified, the function
will be used for split document into different tokens.
:param new_line: whether to use '\\n' ... | get_words_from_document | python | modelscope/data-juicer | data_juicer/ops/common/helper_func.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/common/helper_func.py | Apache-2.0 |
def words_refinement(words,
lower_case=False,
strip_chars=None,
use_words_aug=False,
words_aug_group_sizes=[2],
words_aug_join_char=''):
"""
Refine split words. Non reversible since the document is split on
... |
Refine split words. Non reversible since the document is split on
multiple characters, words are stripped of special characters and
characters are converted to lower case.
:param words: the word list to be augmented
:param lower_case: whether to convert word to lowercase
:param strip_chars: ch... | words_refinement | python | modelscope/data-juicer | data_juicer/ops/common/helper_func.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/common/helper_func.py | Apache-2.0 |
def get_sentences_from_document(document, model_func=None):
"""
Get sentences from a document.
:param document: document that need to split sentences
:param model_func: function of sentence model, if specified, the
function will be used for splitting document into different
sentences.
... |
Get sentences from a document.
:param document: document that need to split sentences
:param model_func: function of sentence model, if specified, the
function will be used for splitting document into different
sentences.
:return: document with the sentences separated by '\\n'
| get_sentences_from_document | python | modelscope/data-juicer | data_juicer/ops/common/helper_func.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/common/helper_func.py | Apache-2.0 |
def split_text_by_punctuation(text):
"""
Split text by any zh and en punctuation
:param text: text to be split.
:return: sub texts split by any zh and en punctuation
"""
# any zh and en punctuation
punctuation_pattern = r'[\u3000-\u303f\uff00-\uffef]|[!"#$%&\'()*+,-./:;<=>?@[\\\]^_`{|}~]' ... |
Split text by any zh and en punctuation
:param text: text to be split.
:return: sub texts split by any zh and en punctuation
| split_text_by_punctuation | python | modelscope/data-juicer | data_juicer/ops/common/helper_func.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/common/helper_func.py | Apache-2.0 |
def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0):
"""
Rescale `noise_cfg` according to `guidance_rescale`. Based on
findings of [Common Diffusion Noise Schedules and
Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
See Section 3.4
"""
std_text = noise_p... |
Rescale `noise_cfg` according to `guidance_rescale`. Based on
findings of [Common Diffusion Noise Schedules and
Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
See Section 3.4
| rescale_noise_cfg | python | modelscope/data-juicer | data_juicer/ops/common/prompt2prompt_pipeline.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/common/prompt2prompt_pipeline.py | Apache-2.0 |
def __init__(self,
lowercase: bool = False,
ignore_non_character: bool = False,
*args,
**kwargs):
"""
Initialization method.
:param lowercase: Whether to convert sample text to lower case
:param ignore_non_character: Wh... |
Initialization method.
:param lowercase: Whether to convert sample text to lower case
:param ignore_non_character: Whether to ignore non-alphabet
characters, including whitespaces, digits, and punctuations
:param args: extra args
:param kwargs: extra args.
| __init__ | python | modelscope/data-juicer | data_juicer/ops/deduplicator/document_deduplicator.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/deduplicator/document_deduplicator.py | Apache-2.0 |
def compute_hash(self, sample):
"""
Compute md5 hash values for the sample.
:param sample: input sample
:return: sample with md5 hash value.
"""
# check if it's computed already
if HashKeys.hash in sample:
return sample
text = sample[self.tex... |
Compute md5 hash values for the sample.
:param sample: input sample
:return: sample with md5 hash value.
| compute_hash | python | modelscope/data-juicer | data_juicer/ops/deduplicator/document_deduplicator.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/deduplicator/document_deduplicator.py | Apache-2.0 |
def process(self, dataset, show_num=0):
"""
For doc-level, dataset --> dataset.
:param dataset: input dataset
:param show_num: number of traced samples used when tracer is
open.
:return: deduplicated dataset and the sampled duplicate pairs.
"""
# no n... |
For doc-level, dataset --> dataset.
:param dataset: input dataset
:param show_num: number of traced samples used when tracer is
open.
:return: deduplicated dataset and the sampled duplicate pairs.
| process | python | modelscope/data-juicer | data_juicer/ops/deduplicator/document_deduplicator.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/deduplicator/document_deduplicator.py | Apache-2.0 |
def optimal_param(
threshold: float,
num_perm: int,
false_positive_weight: float = 0.5,
false_negative_weight: float = 0.5,
):
"""
Compute the optimal `MinHashLSH` parameter that minimizes the weighted sum
of probabilities of false positive and false negative, taken from
datasketch.
... |
Compute the optimal `MinHashLSH` parameter that minimizes the weighted sum
of probabilities of false positive and false negative, taken from
datasketch.
:param threshold: float. The threshold for similarity
:param num_perm: int. The number of permutations
:param false_positive_weight: float. T... | optimal_param | python | modelscope/data-juicer | data_juicer/ops/deduplicator/document_minhash_deduplicator.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/deduplicator/document_minhash_deduplicator.py | Apache-2.0 |
def __init__(
self,
tokenization: str = 'space',
window_size: PositiveInt = 5,
lowercase: bool = True,
ignore_pattern: Optional[str] = None,
num_permutations: PositiveInt = 256,
jaccard_threshold: Annotated[float, Field(ge=0, le=1)] = 0.7,
num_bands: Optio... |
Initialization method.
:param tokenization: tokenization method for sample texts. It
should be one of [space, punctuation, character,
sentencepiece]. For English-like languages, we recommend
to use 'space', for Chinese-like languages, we recommend
to use... | __init__ | python | modelscope/data-juicer | data_juicer/ops/deduplicator/document_minhash_deduplicator.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/deduplicator/document_minhash_deduplicator.py | Apache-2.0 |
def compute_hash(self, sample):
"""
Compute minhash values for the sample.
:param sample: input sample
:return: sample with minhash value.
"""
# check if it's computed already
if HashKeys.minhash in sample:
return sample
text = sample[self.te... |
Compute minhash values for the sample.
:param sample: input sample
:return: sample with minhash value.
| compute_hash | python | modelscope/data-juicer | data_juicer/ops/deduplicator/document_minhash_deduplicator.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/deduplicator/document_minhash_deduplicator.py | Apache-2.0 |
def __init__(self,
tokenization: str = 'space',
window_size: PositiveInt = 6,
lowercase: bool = True,
ignore_pattern: Optional[str] = None,
num_blocks: PositiveInt = 6,
hamming_distance: PositiveInt = 4,
... |
Initialization method :param tokenization: tokenization method for
sample texts.
It should be one of [space, punctuation, character]. For
English-like languages, we recommend to use 'space'. And for
Chinese-like languages, we recommend to use 'character'
:param window_... | __init__ | python | modelscope/data-juicer | data_juicer/ops/deduplicator/document_simhash_deduplicator.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/deduplicator/document_simhash_deduplicator.py | Apache-2.0 |
def compute_hash(self, sample):
"""
Compute simhash values for the sample.
:param sample: input sample
:return: sample with simhash value.
"""
# check if it's computed already
if HashKeys.simhash in sample:
return sample
text = sample[self.te... |
Compute simhash values for the sample.
:param sample: input sample
:return: sample with simhash value.
| compute_hash | python | modelscope/data-juicer | data_juicer/ops/deduplicator/document_simhash_deduplicator.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/deduplicator/document_simhash_deduplicator.py | Apache-2.0 |
def __init__(self,
method: str = 'phash',
consider_text: bool = False,
*args,
**kwargs):
"""
Initialization method.
:param method: hash method for image
:param consider_text: whether to consider text hash together with ... |
Initialization method.
:param method: hash method for image
:param consider_text: whether to consider text hash together with image
hash when applying deduplication.
:param args: extra args
:param kwargs: extra args
| __init__ | python | modelscope/data-juicer | data_juicer/ops/deduplicator/image_deduplicator.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/deduplicator/image_deduplicator.py | Apache-2.0 |
def __init__(self,
backend: str = 'ray_actor',
redis_address: str = 'redis://localhost:6379',
*args,
**kwargs):
"""
Initialization.
:param backend: the backend for dedup, either 'ray_actor' or 'redis'
:param redis_addres... |
Initialization.
:param backend: the backend for dedup, either 'ray_actor' or 'redis'
:param redis_address: the address of redis server
:param args: extra args
:param kwargs: extra args
| __init__ | python | modelscope/data-juicer | data_juicer/ops/deduplicator/ray_basic_deduplicator.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/deduplicator/ray_basic_deduplicator.py | Apache-2.0 |
def get_remote_classes():
"""Get remote versions of classes with Ray decorators applied at runtime."""
# Apply ray.method decorator to get_next_id at runtime
IdGenerator.get_next_id = ray.method(num_returns=2)(
IdGenerator.get_next_id)
return {
'IdGenerator': ray.remote(IdGenerator),
... | Get remote versions of classes with Ray decorators applied at runtime. | get_remote_classes | python | modelscope/data-juicer | data_juicer/ops/deduplicator/ray_bts_minhash_deduplicator.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/deduplicator/ray_bts_minhash_deduplicator.py | Apache-2.0 |
def __init__(
self,
tokenization: str = 'space',
window_size: PositiveInt = 5,
lowercase: bool = True,
ignore_pattern: Optional[str] = None,
num_permutations: PositiveInt = 256,
jaccard_threshold: Annotated[float, Field(ge=0, le=1)] = 0.7,
num_bands: Optio... |
Initialization method.
:param tokenization: tokenization method for sample texts. It
should be one of [space, punctuation, character,
sentencepiece]. For English-like languages, we recommend
to use 'space', for Chinese-like languages, we recommend
to use... | __init__ | python | modelscope/data-juicer | data_juicer/ops/deduplicator/ray_bts_minhash_deduplicator.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/deduplicator/ray_bts_minhash_deduplicator.py | Apache-2.0 |
def __init__(self,
backend: str = 'ray_actor',
redis_address: str = 'redis://localhost:6379',
lowercase: bool = False,
ignore_non_character: bool = False,
*args,
**kwargs):
"""
Initialization method.
... |
Initialization method.
:param backend: the backend for dedup, either 'ray_actor' or 'redis'
:param redis_address: the address of redis server
:param lowercase: Whether to convert sample text to lower case
:param ignore_non_character: Whether to ignore non-alphabet
charac... | __init__ | python | modelscope/data-juicer | data_juicer/ops/deduplicator/ray_document_deduplicator.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/deduplicator/ray_document_deduplicator.py | Apache-2.0 |
def __init__(self, consider_text: bool = False, *args, **kwargs):
"""
Initialization.
:param consider_text: whether to consider text hash together with video
hash when applying deduplication.
:param args: extra args
:param kwargs: extra args
"""
super... |
Initialization.
:param consider_text: whether to consider text hash together with video
hash when applying deduplication.
:param args: extra args
:param kwargs: extra args
| __init__ | python | modelscope/data-juicer | data_juicer/ops/deduplicator/video_deduplicator.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/deduplicator/video_deduplicator.py | Apache-2.0 |
def __init__(self,
tokenization: bool = False,
min_ratio: float = 0.25,
max_ratio: float = sys.maxsize,
*args,
**kwargs):
"""
Initialization method.
:param tokenization: Whether to count the ratio of alphanumer... |
Initialization method.
:param tokenization: Whether to count the ratio of alphanumeric
to the total number of tokens. if tokenization=False, it
will count the ratio of alphanumeric to the total number of
characters.
:param min_ratio: The min filter ratio in ... | __init__ | python | modelscope/data-juicer | data_juicer/ops/filter/alphanumeric_filter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/filter/alphanumeric_filter.py | Apache-2.0 |
def __init__(self,
min_duration: int = 0,
max_duration: int = sys.maxsize,
any_or_all: str = 'any',
*args,
**kwargs):
"""
Initialization method.
:param min_duration: The min audio duration to keep samples in se... |
Initialization method.
:param min_duration: The min audio duration to keep samples in seconds.
It's 0 by default.
:param max_duration: The max audio duration to keep samples in seconds.
It's sys.maxsize by default.
:param any_or_all: keep this sample with 'any' ... | __init__ | python | modelscope/data-juicer | data_juicer/ops/filter/audio_duration_filter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/filter/audio_duration_filter.py | Apache-2.0 |
def __init__(self,
min_snr: float = 0,
max_snr: float = sys.maxsize,
nmf_iter_num: PositiveInt = 500,
any_or_all: str = 'any',
*args,
**kwargs):
"""
Initialization method.
:param min_snr: The m... |
Initialization method.
:param min_snr: The min audio SNR to keep samples in dB. It's 0 by
default.
:param max_snr: The max audio SNR to keep samples in dB. It's
sys.maxsize by default.
:param nmf_iter_num: The max number of iterations to run NMF. It's 500
... | __init__ | python | modelscope/data-juicer | data_juicer/ops/filter/audio_nmf_snr_filter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/filter/audio_nmf_snr_filter.py | Apache-2.0 |
def __init__(self,
min_len: int = 10,
max_len: int = sys.maxsize,
*args,
**kwargs):
"""
Initialization method.
:param min_len: The min filter length in this op, samples will
be filtered if their average line length ... |
Initialization method.
:param min_len: The min filter length in this op, samples will
be filtered if their average line length is below this
parameter.
:param max_len: The max filter length in this op, samples will
be filtered if their average line length ex... | __init__ | python | modelscope/data-juicer | data_juicer/ops/filter/average_line_length_filter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/filter/average_line_length_filter.py | Apache-2.0 |
def __init__(self,
rep_len: PositiveInt = 10,
min_ratio: float = 0.0,
max_ratio: float = 0.5,
*args,
**kwargs):
"""
Initialization method.
:param rep_len: Repetition length for char-level n-gram.
:param... |
Initialization method.
:param rep_len: Repetition length for char-level n-gram.
:param min_ratio: The min filter ratio in this op, samples will
be filtered if their char-level n-gram repetition ratio is
below this parameter.
:param max_ratio: The max filter rati... | __init__ | python | modelscope/data-juicer | data_juicer/ops/filter/character_repetition_filter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/filter/character_repetition_filter.py | Apache-2.0 |
def __init__(self,
lang: str = 'en',
tokenization: bool = False,
max_ratio: float = 0.045,
flagged_words_dir: str = ASSET_DIR,
use_words_aug: bool = False,
words_aug_group_sizes: List[PositiveInt] = [2],
... |
Initialization method.
:param lang: Consider flagged words in what language. If lang ==
"all", we will adopt the one merged from all the available
languages
:param tokenization: Whether to use model to tokenize documents
:param max_ratio: The max filter ratio in... | __init__ | python | modelscope/data-juicer | data_juicer/ops/filter/flagged_words_filter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/filter/flagged_words_filter.py | Apache-2.0 |
def __init__(self, filter_condition: str = '', *args, **kwargs):
"""
Initialization method.
:param filter_condition: The filter condition as a string.
It can include logical operators (and/or) and chain comparisons.
For example: "10 < num <= 30 and text != 'nothing here' ... |
Initialization method.
:param filter_condition: The filter condition as a string.
It can include logical operators (and/or) and chain comparisons.
For example: "10 < num <= 30 and text != 'nothing here' and __dj__meta__.a == 3".
| __init__ | python | modelscope/data-juicer | data_juicer/ops/filter/general_field_filter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/filter/general_field_filter.py | Apache-2.0 |
def __init__(self,
hf_scorer_model: str = '',
trust_remote_code: bool = False,
min_score: float = 0.5,
max_score: float = 1.0,
any_or_all: str = 'any',
*args,
**kwargs):
"""
Initializat... |
Initialization method.
:param hf_scorer_model: Huggingface model name for the aesthetics
predictor. By default, we will use
'shunk031/aesthetics-predictor-v2-sac-logos-ava1-l14-linearMSE',
refer to pypi.org/project/simple-aesthetics-predictor
:param min_scor... | __init__ | python | modelscope/data-juicer | data_juicer/ops/filter/image_aesthetics_filter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/filter/image_aesthetics_filter.py | Apache-2.0 |
def __init__(self,
min_ratio: float = 0.333,
max_ratio: float = 3.0,
any_or_all: str = 'any',
*args,
**kwargs):
"""
Initialization method.
:param min_ratio: The min aspect ratio to keep samples.
:param ... |
Initialization method.
:param min_ratio: The min aspect ratio to keep samples.
:param max_ratio: The max aspect ratio to keep samples.
:param any_or_all: keep this sample with 'any' or 'all' strategy of
all images. 'any': keep this sample if any images meet the
... | __init__ | python | modelscope/data-juicer | data_juicer/ops/filter/image_aspect_ratio_filter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/filter/image_aspect_ratio_filter.py | Apache-2.0 |
def __init__(self,
cv_classifier: str = '',
min_face_count: int = 1,
max_face_count: int = 1,
any_or_all: str = 'any',
*args,
**kwargs):
"""
Initialization method.
:param cv_classifier: OpenCV ... |
Initialization method.
:param cv_classifier: OpenCV classifier path for face detection.
By default, we will use 'haarcascade_frontalface_alt.xml'.
:param min_face_count: Minimum number of faces required for samples.
:param max_face_count: Maximum number of faces required fo... | __init__ | python | modelscope/data-juicer | data_juicer/ops/filter/image_face_count_filter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/filter/image_face_count_filter.py | Apache-2.0 |
def __init__(self,
cv_classifier: str = '',
min_ratio: float = 0.0,
max_ratio: float = 0.4,
any_or_all: str = 'any',
*args,
**kwargs):
"""
Initialization method.
:param cv_classifier: OpenCV cl... |
Initialization method.
:param cv_classifier: OpenCV classifier path for face detection.
By default, we will use 'haarcascade_frontalface_alt.xml'.
:param min_ratio: Min ratio for the largest face area in an image.
:param max_ratio: Max ratio for the largest face area in an ... | __init__ | python | modelscope/data-juicer | data_juicer/ops/filter/image_face_ratio_filter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/filter/image_face_ratio_filter.py | Apache-2.0 |
def __init__(self,
hf_nsfw_model: str = 'Falconsai/nsfw_image_detection',
trust_remote_code: bool = False,
max_score: float = 0.5,
any_or_all: str = 'any',
*args,
**kwargs):
"""
Initialization method.
... |
Initialization method.
:param hf_nsfw_model: nsfw detection model name on huggingface.
:param max_score: the nsfw score threshold for samples.
range from 0 to 1. Samples with nsfw score less than this threshold
will be kept.
:param any_or_all: keep this sample w... | __init__ | python | modelscope/data-juicer | data_juicer/ops/filter/image_nsfw_filter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/filter/image_nsfw_filter.py | Apache-2.0 |
def __init__(self,
hf_clip='openai/clip-vit-base-patch32',
trust_remote_code=False,
min_score: ClosedUnitInterval = 0.1,
max_score: ClosedUnitInterval = 1.0,
any_or_all: str = 'any',
*args,
**kwargs):
... |
Initialization method.
:param hf_clip: clip model name on huggingface to compute
the similarity between image and text.
:param min_score: The min similarity to keep samples.
:param max_score: The max similarity to keep samples.
:param any_or_all: keep this sample wi... | __init__ | python | modelscope/data-juicer | data_juicer/ops/filter/image_pair_similarity_filter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/filter/image_pair_similarity_filter.py | Apache-2.0 |
def __init__(self,
min_width: int = 1,
max_width: int = sys.maxsize,
min_height: int = 1,
max_height: int = sys.maxsize,
any_or_all: str = 'any',
*args,
**kwargs):
"""
Initialization me... |
Initialization method.
:param min_width: The min width to keep samples.
:param max_width: The max width to keep samples.
:param min_height: The min height to keep samples.
:param max_height: The max height to keep samples.
:param any_or_all: keep this sample with 'any' ... | __init__ | python | modelscope/data-juicer | data_juicer/ops/filter/image_shape_filter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/filter/image_shape_filter.py | Apache-2.0 |
def __init__(self,
hf_blip: str = 'Salesforce/blip-itm-base-coco',
trust_remote_code: bool = False,
min_score: float = 0.003,
max_score: float = 1.0,
horizontal_flip: bool = False,
vertical_flip: bool = False,
... |
Initialization method.
:param hf_blip: blip model name on huggingface to compute
the matching score between image and text.
:param min_score: The min matching score to keep samples.
:param max_score: The max matching score to keep samples.
:param horizontal_flip: Fl... | __init__ | python | modelscope/data-juicer | data_juicer/ops/filter/image_text_matching_filter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/filter/image_text_matching_filter.py | Apache-2.0 |
def __init__(self,
hf_clip: str = 'openai/clip-vit-base-patch32',
trust_remote_code: bool = False,
min_score: float = 0.1,
max_score: float = 1.0,
horizontal_flip: bool = False,
vertical_flip: bool = False,
... |
Initialization method.
:param hf_clip: clip model name on huggingface to compute
the similarity between image and text.
:param min_score: The min similarity to keep samples.
:param max_score: The max similarity to keep samples.
:param horizontal_flip: Flip image hor... | __init__ | python | modelscope/data-juicer | data_juicer/ops/filter/image_text_similarity_filter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/filter/image_text_similarity_filter.py | Apache-2.0 |
def __init__(self,
hf_watermark_model: str = 'amrul-hzz/watermark_detector',
trust_remote_code: bool = False,
prob_threshold: float = 0.8,
any_or_all: str = 'any',
*args,
**kwargs):
"""
Initialization m... |
Initialization method.
:param hf_watermark_model: watermark detection model name on
huggingface.
:param prob_threshold: the predicted watermark probability threshold
for samples. range from 0 to 1. Samples with watermark probability
less than this threshold ... | __init__ | python | modelscope/data-juicer | data_juicer/ops/filter/image_watermark_filter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/filter/image_watermark_filter.py | Apache-2.0 |
def __init__(self,
lang: Union[str, List[str]] = '',
min_score: float = 0.8,
*args,
**kwargs):
"""
Initialization method.
:param lang: Samples in which languages to keep.
:param min_score: The min language identificatio... |
Initialization method.
:param lang: Samples in which languages to keep.
:param min_score: The min language identification confidence
scores of samples to keep.
:param args: extra args
:param kwargs: extra args
| __init__ | python | modelscope/data-juicer | data_juicer/ops/filter/language_id_score_filter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/filter/language_id_score_filter.py | Apache-2.0 |
def __init__(self,
api_or_hf_model: str = 'gpt-4o',
min_score: float = 0.5,
is_hf_model: bool = False,
*,
api_endpoint: Optional[str] = None,
response_path: Optional[str] = None,
input_keys: List[str] ... |
Initialization method.
:param api_or_hf_model: API or huggingface model name.
:param min_score: The lowest difficulty score threshold to keep
the sample.
:param api_endpoint: URL endpoint for the API.
:param response_path: Path to extract content from the API respon... | __init__ | python | modelscope/data-juicer | data_juicer/ops/filter/llm_difficulty_score_filter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/filter/llm_difficulty_score_filter.py | Apache-2.0 |
def __init__(self,
api_or_hf_model: str = 'gpt-4o',
min_score: float = 0.5,
is_hf_model: bool = False,
*,
api_endpoint: Optional[str] = None,
response_path: Optional[str] = None,
input_keys: List[str] ... |
Initialization method.
:param api_or_hf_model: API or huggingface model name.
:param min_score: The lowest quality score threshold to keep the
sample.
:param api_endpoint: URL endpoint for the API.
:param response_path: Path to extract content from the API response.... | __init__ | python | modelscope/data-juicer | data_juicer/ops/filter/llm_quality_score_filter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/filter/llm_quality_score_filter.py | Apache-2.0 |
def __init__(self,
min_len: int = 10,
max_len: int = sys.maxsize,
*args,
**kwargs):
"""
Initialization method.
:param min_len: The min filter length in this op, samples will
be filtered if their maximum line length ... |
Initialization method.
:param min_len: The min filter length in this op, samples will
be filtered if their maximum line length is below this
parameter.
:param max_len: The max filter length in this op, samples will
be filtered if their maximum line length ex... | __init__ | python | modelscope/data-juicer | data_juicer/ops/filter/maximum_line_length_filter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/filter/maximum_line_length_filter.py | Apache-2.0 |
def __init__(self,
lang: str = 'en',
max_ppl: float = 1500,
*args,
**kwargs):
"""
Initialization method.
:param lang: Compute perplexity for samples in which language.
:param max_ppl: The max filter perplexity in this o... |
Initialization method.
:param lang: Compute perplexity for samples in which language.
:param max_ppl: The max filter perplexity in this op, samples
will be filtered if their perplexity exceeds this parameter.
:param args: extra args
:param kwargs: extra args
... | __init__ | python | modelscope/data-juicer | data_juicer/ops/filter/perplexity_filter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/filter/perplexity_filter.py | Apache-2.0 |
def __init__(self,
hf_owlvit: str = 'google/owlvit-base-patch32',
trust_remote_code: bool = False,
min_recall: float = 0.1,
max_recall: float = 1.0,
horizontal_flip: bool = False,
vertical_flip: bool = False,
... |
Initialization method.
:param hf_owlvit: Owl-ViT model name on huggingface to locate the
phrases extracted from the text.
:param min_recall: The min phrase grounding recall to keep samples.
:param max_recall: The max phrase grounding recall to keep samples.
:param h... | __init__ | python | modelscope/data-juicer | data_juicer/ops/filter/phrase_grounding_recall_filter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/filter/phrase_grounding_recall_filter.py | Apache-2.0 |
def __init__(self,
min_ratio: float = 0.0,
max_ratio: float = 0.25,
*args,
**kwargs):
"""
Initialization method.
:param min_ratio: The min filter ratio in this op, samples will
be filtered if their special-char rati... |
Initialization method.
:param min_ratio: The min filter ratio in this op, samples will
be filtered if their special-char ratio is below this
parameter.
:param max_ratio: The max filter ratio in this op, samples will
be filtered if their special-char ratio ex... | __init__ | python | modelscope/data-juicer | data_juicer/ops/filter/special_characters_filter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/filter/special_characters_filter.py | Apache-2.0 |
def __init__(self,
field_key: str = '',
target_value: List = [],
*args,
**kwargs):
"""
Initialization method.
:param field_key: Filter based on the specified value
corresponding to the target key. The target key
... |
Initialization method.
:param field_key: Filter based on the specified value
corresponding to the target key. The target key
corresponding to multi-level field information need to be
separated by '.'.
:param target_value: The range of specified field informa... | __init__ | python | modelscope/data-juicer | data_juicer/ops/filter/specified_field_filter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/filter/specified_field_filter.py | Apache-2.0 |
def __init__(self,
field_key: str = '',
min_value: float = -sys.maxsize,
max_value: float = sys.maxsize,
*args,
**kwargs):
"""
Initialization method.
:param field_key: Filter based on the specified numeric valu... |
Initialization method.
:param field_key: Filter based on the specified numeric value
corresponding to the target key. The target key
corresponding to multi-level field information need to be
separated by '.'.
:param min_value: The min filter value in Specifi... | __init__ | python | modelscope/data-juicer | data_juicer/ops/filter/specified_numeric_field_filter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/filter/specified_numeric_field_filter.py | Apache-2.0 |
def __init__(self,
lang: str = 'en',
tokenization: bool = False,
min_ratio: float = 0.3,
stopwords_dir: str = ASSET_DIR,
use_words_aug: bool = False,
words_aug_group_sizes: List[PositiveInt] = [2],
wor... |
Initialization method.
:param lang: Consider stopwords in what language. If lang ==
"all", we will adopt the one merged from all the available
languages
:param tokenization: whether to use model to tokenize documents
:param min_ratio: The min filter ratio in thi... | __init__ | python | modelscope/data-juicer | data_juicer/ops/filter/stopwords_filter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/filter/stopwords_filter.py | Apache-2.0 |
def __init__(self, suffixes: Union[str, List[str]] = [], *args, **kwargs):
"""
Initialization method.
:param suffixes: the suffix of text that will be keep.
For example: '.txt', 'txt' or ['txt', '.pdf', 'docx']
:param args: extra args
:param kwargs: extra args
... |
Initialization method.
:param suffixes: the suffix of text that will be keep.
For example: '.txt', 'txt' or ['txt', '.pdf', 'docx']
:param args: extra args
:param kwargs: extra args
| __init__ | python | modelscope/data-juicer | data_juicer/ops/filter/suffix_filter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/filter/suffix_filter.py | Apache-2.0 |
def __init__(self,
lang: str = 'en',
min_action_num: int = 1,
*args,
**kwargs):
"""
Initialization method.
:param lang: language of the text in the samples. 'en' for detection of
actions in English and 'zh' for dete... |
Initialization method.
:param lang: language of the text in the samples. 'en' for detection of
actions in English and 'zh' for detection of actions in Chinese.
:param mini_action_num: The min action number in the filtering. samples
will be filtered if their action numbe... | __init__ | python | modelscope/data-juicer | data_juicer/ops/filter/text_action_filter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/filter/text_action_filter.py | Apache-2.0 |
def __init__(self,
lang: str = 'en',
min_dependency_num: int = 1,
any_or_all: str = 'all',
*args,
**kwargs):
"""
Initialization method.
:param lang: language of the text in the samples. 'en' for detection of
... |
Initialization method.
:param lang: language of the text in the samples. 'en' for detection of
entities in English and 'zh' for detection of entities in Chinese.
:param mini_dependency_num: The min token number in the filtering.
Objects is independent if their number of... | __init__ | python | modelscope/data-juicer | data_juicer/ops/filter/text_entity_dependency_filter.py | https://github.com/modelscope/data-juicer/blob/master/data_juicer/ops/filter/text_entity_dependency_filter.py | Apache-2.0 |
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