命名实体识别python代码

以下是一个用于命名实体识别的Python代码示例：

from transformers import AutoTokenizer
import torch

# 加载分词器
tokenizer = AutoTokenizer.from_pretrained('hfl/rbt6')

# 数据整理函数
def collate_fn(data):
    tokens = \[i\[0\] for i in data\]
    labels = \[i\[1\] for i in data\]
    inputs = tokenizer.batch_encode_plus(tokens, truncation=True, padding=True, return_tensors='pt', is_split_into_words=True)
    lens = inputs\['input_ids'\].shape\[1\]
    for i in range(len(labels)):
        labels\[i\] = \[7\] + labels\[i\]
        labels\[i\] += \[7\] * lens
        labels\[i\] = labels\[i\]\[:lens\]
    return inputs, torch.LongTensor(labels)

这段代码使用了Hugging Face的transformers库，其中`AutoTokenizer`用于加载预训练的中文BERT模型的分词器。`collate_fn`函数用于整理数据，将输入的句子进行分词，并对标签进行处理，以适应命名实体识别任务的训练。
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