使用pytorch实现基于CRF的CoNLL-2003命名实体识别，请给出完整代码

在PyTorch中实现基于条件随机场（Conditional Random Field, CRF）的CoNLL-2003命名实体识别，首先需要安装必要的库，如`spacy`、`conll`、`torch`等。以下是一个简单的例子，展示了如何构建CRF层并将其应用到一个双向LSTM模型上：

import torch
from torch import nn
import spacy
from conllu import parse_incr
from collections import defaultdict

# 定义CRF层
class ConditionalRandomField(nn.Module):
    def __init__(self, num_tags, transitions=None):
        super(ConditionalRandomField, self).__init__()
        if transitions is None:
            # 初始化默认的转移概率矩阵
            transitions = torch.zeros(num_tags, num_tags)
        self.transitions = nn.Parameter(transitions)

    def forward(self, emissions, mask):
        # emissions: (batch_size, seq_len, num_tags)
        # mask: (batch_size, seq_len) 判断哪些位置是有标签的
        batch_size, seq_len, _ = emissions.size()
        
        # 计算分数
        scores = emissions + self.transitions.view(1, -1).expand(batch_size, seq_len, num_tags)
        scores = scores * mask.unsqueeze(-1).float()

        # 状态序列的初始概率
        init_scores = torch.log(torch.sum(torch.exp(scores[:, 0, :]), dim=1))

        # 动态规划计算路径得分
        v = torch.zeros(batch_size)
        for i in range(1, seq_len):
            next_scores = scores[:, i, :]
            trans_score = self.transitions[range(batch_size), tag_seq[:, i - 1]]
            v = v + torch.logsumexp(next_scores + trans_score.unsqueeze(1), dim=-1)

        return init_scores, v

# 示例代码，假设我们已经有了一个预训练的词嵌入和一个双向LSTM模型
nlp = spacy.load('en_core_web_sm')
def get_bilstm_crf_model(input_dim, hidden_dim, num_tags):
    lstm = nn.LSTM(input_dim, hidden_dim, bidirectional=True)
    linear = nn.Linear(hidden_dim * 2, num_tags)
    crf = ConditionalRandomField(num_tags)

    model = nn.Sequential(lstm, linear, crf)
    return model

# 假设输入数据和标签数据已经准备好了
inputs = ...  # 形状应该是(batch_size, seq_len, input_dim)
tags = ...  # 形状应该是(batch_size, seq_len)

# 将输入转换成合适的维度
input_emissions = ...  # 使用词嵌入和编码后的向量得到发射概率
tag_seq, _ = ...  # 序列化标签以便用于CRF

# 高效地创建mask，标记有效的位置
mask = torch.ones_like(tag_seq, dtype=torch.uint8)
mask[tag_seq == 'O'] = 0  # 标记非实体位置为0

model = get_bilstm_crf_model(input_dim, hidden_dim, num_tags)
output_scores, viterbi_path = model(input_emissions, mask)

# 对应于viterbi算法，找到最可能的路径
best_paths = viterbi_path.argmax(dim=1)