IDSF_BERT

This is a BERT-based model for Joint Intent Detection and Slot Filling (IDSF).

Model Description

• Model Type: BERT-based Joint Intent Detection and Slot Filling
• Custom Architecture: BertIDSF with intent and slot classification heads
• Language: English

Usage

import torch
import json
from transformers import AutoTokenizer, BertConfig
from transformers.models.bert.modeling_bert import BertPreTrainedModel, BertModel
from torch import nn

# First define the model architecture
class IntentClassifier(nn.Module):
    def __init__(self, input_dim, num_intent_labels, dropout_rate=0.):
        super(IntentClassifier, self).__init__()
        self.dropout = nn.Dropout(dropout_rate)
        self.linear = nn.Linear(input_dim, num_intent_labels)
        
    def forward(self, x):
        x = self.dropout(x)
        return self.linear(x)

class SlotClassifier(nn.Module):
    def __init__(self, input_dim, num_slot_labels, dropout_rate=0.):
        super(SlotClassifier, self).__init__()
        self.dropout = nn.Dropout(dropout_rate)
        self.linear = nn.Linear(input_dim, num_slot_labels)
        
    def forward(self, x):
        x = self.dropout(x)
        return self.linear(x)

class BertIDSF(BertPreTrainedModel):
    def __init__(self, config, intent_label_lst, slot_label_lst, n_layers=1):
        super().__init__(config)
        self.num_intent_labels = len(intent_label_lst)
        self.num_slot_labels = len(slot_label_lst)
        self.bert = BertModel(config=config)
        
        # Store dictionaries in config for later use
        self.config.dict2 = {str(idx+1): label for idx, label in enumerate(slot_label_lst)}
        self.config.inte2 = {str(idx+1): label for idx, label in enumerate(intent_label_lst)}
        
        classifier_dropout = (
            config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob
        )
        self.dropout = nn.Dropout(classifier_dropout)
        self.intent_classifier = IntentClassifier(config.hidden_size, self.num_intent_labels)
        self.slot_classifier = SlotClassifier(config.hidden_size, self.num_slot_labels)

    def forward(
            self,
            input_ids=None,
            attention_mask=None,
            token_type_ids=None,
            position_ids=None,
            head_mask=None,
            inputs_embeds=None,
            labels=None,
            intents=None,
            output_attentions=True,
            lens=None,
            device=None
    ):
        outputs = self.bert(
            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=True
        )
        
        sequence_output = outputs[0]
        sequence_output = self.dropout(sequence_output)
        
        intent_logits = self.intent_classifier(sequence_output[:, 0, :])
        slot_logits = self.slot_classifier(sequence_output)
        
        total_loss = 0
        
        # Intent Softmax
        if intents is not None:
            intent_loss_fct = nn.CrossEntropyLoss()
            intent_loss = intent_loss_fct(intent_logits.view(-1, self.num_intent_labels), intents.view(-1))
            total_loss += 0.5 * intent_loss
            
        # Slot Softmax
        if labels is not None:
            slot_loss_fct = nn.CrossEntropyLoss(ignore_index=0)
            # Only keep active parts of the loss
            if attention_mask is not None:
                active_loss = attention_mask.view(-1) == 1
                active_logits = slot_logits.view(-1, self.num_slot_labels)[active_loss]
                active_labels = labels.view(-1)[active_loss]
                slot_loss = slot_loss_fct(active_logits, active_labels)
            else:
                slot_loss = slot_loss_fct(slot_logits.view(-1, self.num_slot_labels), labels.view(-1))
            total_loss += 0.5 * slot_loss
            
        outputs = ((intent_logits, slot_logits),) + outputs[2:]  # add hidden states and attention if they are here
        outputs = (total_loss,) + outputs
        
        return outputs  # (loss), scores, (hidden_states), (attentions)

# Now load and use the model
model_path = "soltaniali/IDSF_BERT"

# Load dictionaries from JSON files
with open('dict2.json', 'r') as f:
    dict2 = json.load(f)
with open('inte2.json', 'r') as f:
    inte2 = json.load(f)

# Initialize tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_path)
config = BertConfig.from_pretrained(model_path)
model = BertIDSF.from_pretrained(
    model_path,
    config=config,
    slot_label_lst=list(dict2.values()),
    intent_label_lst=list(inte2.values())
)

# Process a sentence
sentence = "I want to transfer 200 dollars to my savings account"
# ... process with your IDSFService class

Important Note

This model uses a custom architecture (BertIDSF) and requires both the class definition and dictionaries to be loaded correctly.