NERX 0.2.1

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.dataset import DictDataset
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']
num_classes = len(classes)
    
def f(data):
    return 5 < len(data['tokens']) <= 512 - 2

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 = DictDataset(train_set, 'tokens', 'ner_tags')
val_set = DictDataset(val_set, 'tokens', 'ner_tags')

model = NER(pretrained_path, num_classes=num_classes, num_train_layers=2)
wrapper = ClassifyModelWrapper(model)
tokenizer = AutoTokenizer.from_pretrained(pretrained_path)
history = wrapper.train(train_set, val_set, collate_fn=Collator(tokenizer, num_classes - 1))
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 = 50, 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.inference_mode():
        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)