yaset.single¶

yaset.single.apply¶

yaset.single.apply.apply_model(model_dir: str = None, input_file: str = None, output_file: str = None, batch_size: int = 128, cuda: bool = False, n_jobs: int = None, debug: bool = False)¶

yaset.single.apply.chunks(l, n)¶

yaset.single.inference¶

class yaset.single.inference.NERModel(mappings: dict = None, model: torch.nn.modules.module.Module = None, options: dict = None, model_dir: str = None)¶

Bases: object

collate_sentences(batch)¶

dev_predict(sentences, cuda, *arg, **kwargs)¶

sentence_to_ids(sentence)¶

yaset.single.train¶

yaset.single.train.create_dataloader(mappings: Dict[KT, VT] = None, options: Dict[KT, VT] = None, instance_json_file: str = None, test: bool = False, working_dir: str = None) → Tuple[torch.utils.data.dataloader.DataLoader, int, torch.utils.data.dataset.Dataset]¶

yaset.single.train.train_single_model(option_file: str = None, output_dir: str = None) → None¶

Train a NER model

Parameters:	option_file (str) – model configuration file (jsonnet format) output_dir (str) – model output directory
Returns:	None