NERX 0.1.3

Named Entity Recognition

Usage Sample ''''''''''''

.. code:: python

    import torch
    from torch.utils.data import Dataset
    from transformers import AutoTokenizer
    from nerx import NER, Collator
    from model_wrapper import ClassifyModelWrapper

    pretrained_path = "nghuyong/ernie-3.0-base-zh"
    classes = ['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC', 'padding']

    def f(data):
            return 5 < len(data['tokens']) <= 512 - 2

    class PairDataset(Dataset):

            def __init__(self, dataset):
                    self.dataset = dataset

            def __getitem__(self, index):
                    data = self.dataset[index]
                    return data['tokens'], data['ner_tags']

            def __len__(self):
                    return len(self.dataset)

    dataset_dict = load_from_disk('/kaggle/input/peoples-daily-ner-data/peoples_daily_ner')
    train_set = dataset_dict['train'].remove_columns(['id']).filter(f, cache_file_name='/kaggle/working/train.cache')
    val_set = dataset_dict['validation'].remove_columns(['id']).filter(f, cache_file_name='/kaggle/working/val.cache')
    test_set = dataset_dict['test'].remove_columns(['id']).filter(f, cache_file_name='/kaggle/working/test.cache')

    train_set = PairDataset(train_set)
    val_set = PairDataset(val_set)
    
    model = NER(pretrained_path, num_classes=8, num_train_layers=2)
    wrapper = ClassifyModelWrapper(model)
    tokenizer = AutoTokenizer.from_pretrained(pretrained_path)
    history = wrapper.train(train_set, val_set, collate_fn=Collator(tokenizer, label_padding_id=7))
    wrapper.save_state_dict(mode='best')

    def display(tags, text, classes):
            padding_idx = len(classes) - 1
            start_index, start_tag = -1, -1
            for i, tag in enumerate(tags):
                    if tag == padding_idx:
                    if start_index != -1:
                            print(f"{start_index}-{i}", ' ', classes[start_tag].split('-')[1], ' ', ''.join(text[start_index:i]))
                    break

                    if 0 < tag:
                    if start_index == -1 and 0 < tag:
                            start_index, start_tag = i, tag
                            continue
                            
                    if start_tag != tag - 1 and start_tag != tag:
                            print(f"{start_index}-{i}", ' ', classes[start_tag].split('-')[1], ' ', ''.join(text[start_index:i]))
                            start_index, start_tag = i, tag 
                    else:
                    if start_index > -1:
                            print(f"{start_index}-{i}", ' ', classes[start_tag].split('-')[1], ' ', ''.join(text[start_index:i]))
                            start_index, start_tag = -1, -1

    def test(data, model):
            M, N = 40, 30
            text, label = data['tokens'], data['ner_tags']
            tokens = tokenizer.batch_encode_plus([text],
                                            max_length=256,
                                            padding=True,
                                            truncation=True,
                                            return_tensors='pt',
                                            return_token_type_ids=False,
                                            is_split_into_words=True)
            model.eval()
            with torch.no_grad():
                    result = model(tokens)[0]
            print('=' * M, "原文", '=' * M)
            print(''.join(text))
            print('-' * N, "标注",'-' * N)
            display(label, text, classes)
            print('-' * N, "预测",'-' * N)
            display(result, text, classes)

    for i in range(20):
            test(test_set[i], model)