pine.quantitative_evaluation package

Subpackages

• pine.quantitative_evaluation.language_modeling package
  • Submodules
  • pine.quantitative_evaluation.language_modeling.data module
  • pine.quantitative_evaluation.language_modeling.language_modeling module
  • pine.quantitative_evaluation.language_modeling.model module
  • pine.quantitative_evaluation.language_modeling.training module
  • pine.quantitative_evaluation.language_modeling.view module
  • Module contents
• pine.quantitative_evaluation.text_classification package
  • Submodules
  • pine.quantitative_evaluation.text_classification.data module
  • pine.quantitative_evaluation.text_classification.evaluation module
  • pine.quantitative_evaluation.text_classification.text_classification module
  • Module contents

Submodules

pine.quantitative_evaluation.word_analogy module

class pine.quantitative_evaluation.word_analogy.Result(result: Tuple[float, List[Dict[str, List[Tuple[str, str, str, str]]]]], language_model: pine.language_model.LanguageModel)

Bases: object

The results of a word analogy task for a log-bilinear language model.

Parameters

• result (RawResult) – Results of a word analogy task.

• language_model (LanguageModel) – A log-bilinear language model.

Variables

• result (RawResult) – Results of a word analogy task.

• language_model (LanguageModel) – A log-bilinear language model.

pine.quantitative_evaluation.word_analogy.evaluate(dataset_path: pathlib.Path, language_model: pine.language_model.LanguageModel) → pine.quantitative_evaluation.word_analogy.Result

pine.quantitative_evaluation.word_analogy.get_dataset_path(language: str, dataset_dir: pathlib.Path) → pathlib.Path

Module contents

pine.quantitative_evaluation.LanguageModelingResult

alias of pine.quantitative_evaluation.language_modeling.language_modeling.Result

pine.quantitative_evaluation.TextClassificationResult

alias of pine.quantitative_evaluation.text_classification.text_classification.Result

pine.quantitative_evaluation.WordAnalogyResult

alias of pine.quantitative_evaluation.word_analogy.Result

pine.quantitative_evaluation.evaluate_language_modeling(dataset_paths: Dict[str, pathlib.Path], language_model: pine.language_model.LanguageModel) → pine.quantitative_evaluation.language_modeling.language_modeling.Result

pine.quantitative_evaluation.evaluate_text_classification(dataset_path: pathlib.Path, language_model: pine.language_model.LanguageModel, method: str) → pine.quantitative_evaluation.text_classification.text_classification.Result

pine.quantitative_evaluation.evaluate_word_analogy(dataset_path: pathlib.Path, language_model: pine.language_model.LanguageModel) → pine.quantitative_evaluation.word_analogy.Result

pine.quantitative_evaluation.get_language_modeling_dataset(language: str, dataset_dir: pathlib.Path) → Dict[str, pathlib.Path]

pine.quantitative_evaluation.get_text_classification_datasets(language: str, dataset_dir: pathlib.Path) → List[pathlib.Path]

pine.quantitative_evaluation.get_word_analogy_dataset(language: str, dataset_dir: pathlib.Path) → pathlib.Path

pine.quantitative_evaluation.plot_language_modeling_results(*language_models: pine.language_model.LanguageModel, kind: Optional[str] = None, subset: Optional[str] = None) → matplotlib.figure.Figure