Module

deepke.name_entity_re.few_shot.module.datasets module

class deepke.name_entity_re.few_shot.module.datasets.ConllNERProcessor(data_path, mapping, bart_name, learn_weights)

Bases: object

load_from_file(mode='train')

load conll ner from file

Parameters

mode (str, optional) – train/test/dev. Defaults to ‘train’.

Returns

outputs (dict) raw_words: [‘EU’, ‘rejects’, ‘German’, ‘call’, ‘to’, ‘boycott’, ‘British’, ‘lamb’, ‘.’] raw_targets: [‘B-ORG’, ‘O’, ‘B-MISC’, ‘O’, ‘O’, ‘O’, ‘B-MISC’, ‘O’, ‘O’] entities: [[‘EU’], [‘German’], [‘British’]] entity_tags: [‘org’, ‘misc’, ‘misc’] entity_spans: [[0, 1], [2, 3], [6, 7]]

process(data_dict)

class deepke.name_entity_re.few_shot.module.datasets.ConllNERDataset(data_processor, mode='train')

Bases: torch.utils.data.dataset.Dataset

collate_fn(batch)

deepke.name_entity_re.few_shot.module.mapping_type module

deepke.name_entity_re.few_shot.module.metrics module

class deepke.name_entity_re.few_shot.module.metrics.Seq2SeqSpanMetric(eos_token_id, num_labels, target_type='word')

Bases: object

evaluate(target_span, pred, tgt_tokens)

get_metric(reset=True)

deepke.name_entity_re.few_shot.module.train module

class deepke.name_entity_re.few_shot.module.train.Trainer(train_data=None, dev_data=None, test_data=None, model=None, process=None, args=None, logger=None, loss=None, metrics=None, writer=None)

Bases: object

train()

evaluate(epoch)

predict()

before_train()