搭建RoBERTa + BiLSTM + CRF模型的python代码

在Python中搭建RoBERTa（Robustly Optimized BERT Pretraining Approach）+ BiLSTM (Bidirectional Long Short-Term Memory) + CRF (Conditional Random Field)模型通常用于序列标注任务，如命名实体识别(NER)。下面是一个简化版的示例，使用Hugging Face的Transformers库和Keras或PyTorch。这里假设你已经安装了transformers、torch等库。

**使用PyTorch的例子**:

import torch
from torch import nn
from transformers import RobertaTokenizerFast, RobertaModel

class RoBERTaBiLSTMCRF(nn.Module):
    def __init__(self, num_tags, roberta_model_name='roberta-base'):
        super(RoBERTaBiLSTMCRF, self).__init__()
        self.roberta = RobertaModel.from_pretrained(roberta_model_name)
        self.dropout = nn.Dropout(0.5)
        self.lstm = nn.LSTM(self.roberta.config.hidden_size, 128, bidirectional=True, batch_first=True)
        self.linear = nn.Linear(256, num_tags) # 128 (forward) + 128 (backward)
        self.crf = nn.CRF(num_tags, batch_first=True)

    def forward(self, input_ids, attention_mask):
        _, pooled_output = self.roberta(input_ids=input_ids, attention_mask=attention_mask)
        pooled_output = self.dropout(pooled_output)
        lstm_out, _ = self.lstm(pooled_output)
        tag_space = self.linear(lstm_out[:, -1]) # 取最后一个时间步作为输入到线性层
        crf_input = tag_space if not self.training else tag_space.view(-1, self.num_tags) # 如果在训练阶段，需要转为适合CRF的格式
        return self.crf.decode(crf_input, attention_mask), crf_input

# 假设num_tags是你的标签数量，你可以根据实际数据创建model实例并训练
num_tags = ...  # 例如，如果你有5个标签类别
model = RoBERTaBiLSTMCRF(num_tags)

**使用Keras的例子**:

from transformers import TFAutoModel, Tokenizer
from tensorflow.keras.layers import Dense, Input, LSTM, Dropout
from tensorflow.keras.utils import Sequence
from tensorflow.keras.preprocessing.sequence import pad_sequences

class CRFWrapper(tf.keras.Model):
    def __init__(self, crf, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.crf = crf

    def call(self, inputs, mask=None):
        features = inputs
        emission_scores = self.crf.call(features)
        viterbi_sequence, _ = self.crf.decode(emission_scores, mask)
        return viterbi_sequence

# 假设你已经有了预训练的RoBERTa模型和Tokenizer
tokenizer = ...
roberta_model = ...

input_ids = ...  # 输入的Token IDs
attention_mask = ...  # 输入的注意力掩码

# 构建模型结构
input_ids = Input(shape=(None,), dtype=tf.int32, name="input_ids")
attention_mask = Input(shape=(None,), dtype=tf.int32, name="attention_mask")

sequence_output, _ = roberta_model(inputs=[input_ids, attention_mask])
lstm_output = LSTM(units=128, return_sequences=True)(sequence_output)
dropout_layer = Dropout(rate=0.5)(lstm_output)
linear_layer = Dense(units=num_tags)(dropout_layer)
crf = CRFWrapper(num_tags)  # 需要自定义CRF层
output = crf(linear_layer)

model = tf.keras.Model(inputs=[input_ids, attention_mask], outputs=output)

# 训练模型
model.compile(optimizer="adam", loss=crf.loss_function, metrics=[crf.accuracy])

# 使用Sequence生成器适配batch训练