1.9 KiB
Utilities
from great_ai.utilities import *
NLP tools
Well-tested tools that can be used in production with confidence. The toolbox of feature-extraction functions is expected to grow to cover other domains as well.
::: great_ai.utilities.clean ::: great_ai.utilities.get_sentences ::: great_ai.utilities.language.predict_language ::: great_ai.utilities.language.english_name_of_language ::: great_ai.utilities.language.is_english ::: great_ai.utilities.evaluate_ranking.evaluate_ranking
Parallel processing
Multiprocessing and multithreading-based parallelism with support for async functions. Its main purpose is to implement [great_ai.GreatAI.process_batch][], however, the parallel processing functions are also convenient for covering other types of mapping needs with a friendlier API than joblib{ target=_blank } or multiprocess{ target=_blank }.
::: great_ai.utilities.simple_parallel_map options: show_root_heading: true ::: great_ai.utilities.parallel_map.parallel_map ::: great_ai.utilities.threaded_parallel_map options: show_root_heading: true
Composable parallel processing
Because both [threaded_parallel_map][great_ai.utilities.parallel_map.threaded_parallel_map.threaded_parallel_map] and [parallel_map][great_ai.utilities.parallel_map.parallel_map.parallel_map] have a streaming interface, it is easy to compose them and end up with, for example, a process for each CPU core with its own thread-pool or event-loop. Longer pipelines are also easy to imagine. The chunking methods help in these compositions.
For more info, check-out the scraping guide.
::: great_ai.utilities.chunk ::: great_ai.utilities.unchunk
Operations
::: great_ai.utilities.ConfigFile options: show_root_heading: true
::: great_ai.utilities.get_logger options: show_root_heading: true