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NLU/NLU_model.py

+from transformers import BertPreTrainedModel, BertModel
+from torch import nn
+
+
+class NLUModule(BertPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_intent_labels = config.num_intent_labels
+        self.num_slot_labels = config.num_slot_labels
+
+        self.bert = BertModel(config)
+        self.dropout = nn.Dropout(config.hidden_dropout_prob)
+        self.intent_classifier = nn.Linear(config.hidden_size, config.num_intent_labels)
+        self.slot_classifier = nn.Linear(config.hidden_size, config.num_slot_labels)
+
+        self.init_weights()
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        output_attentions=None,
+        output_hidden_states=None,
+    ):
+        r"""
+        labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size,)`, `optional`):
+            Labels for computing the sequence classification/regression loss.
+            Indices should be in :obj:`[0, ..., config.num_labels - 1]`.
+            If :obj:`config.num_labels == 1` a regression loss is computed (Mean-Square loss),
+            If :obj:`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        #######################################################
+        # TODO: Complete the following function.
+        #       The following function should return the logits of intent and slot classification.
+        #       You can implement this function with the following steps:
+        #       1. Forward the input to BERT model
+        #       2. Extract the representation of the whole sentence and each tokens
+        #       3. Feed the representation of the whole sentence and each tokens to the corresponding classifier
+        #######################################################
+        outputs = self.bert(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+        )
+
+        seq_encoding = outputs[0]
+        pooled_output = outputs[1]
+
+        pooled_output = self.dropout(pooled_output)
+        intent_logits = self.intent_classifier(pooled_output)
+        slot_logits = self.slot_classifier(seq_encoding)
+
+        return intent_logits, slot_logits