Update routes/predict_batch.py

Rename Sentiment/ml/model/multitask_bert.pyimport torch import torch.nn as nn from transformers import AutoConfig, AutoModel class MultiTaskBert(nn.Module): def __init__( self, model_name: str, n_sentiment: int, n_intent: int, n_topic: int, dropout: float = 0.2, init_from_pretrained: bool = False, # IMPORTANT ): super().__init__() self.model_name = model_name if init_from_pretrained: # trust_remote_code lets us load architectures (e.g., ModernBERT/mmBERT) # that may not be bundled with the installed transformers version. self.encoder = AutoModel.from_pretrained( model_name, trust_remote_code=True, ) else: config = AutoConfig.from_pretrained( model_name, trust_remote_code=True, ) self.encoder = AutoModel.from_config( config, trust_remote_code=True, ) hidden_size = self.encoder.config.hidden_size self.dropout = nn.Dropout(dropout) self.sentiment_head = nn.Linear(hidden_size, n_sentiment) self.intent_head = nn.Linear(hidden_size, n_intent) self.topic_head = nn.Linear(hidden_size, n_topic) def forward(self, input_ids, attention_mask): outputs = self.encoder( input_ids=input_ids, attention_mask=attention_mask, ) pooled = outputs.last_hidden_state[:, 0] # CLS pooled = self.dropout(pooled) return ( self.sentiment_head(pooled), self.intent_head(pooled), self.topic_head(pooled), ) to Sentiment/ml/model/multitask_bert.py

Create multitask_bert.pyimport torch import torch.nn as nn from transformers import AutoConfig, AutoModel class MultiTaskBert(nn.Module): def __init__( self, model_name: str, n_sentiment: int, n_intent: int, n_topic: int, dropout: float = 0.2, init_from_pretrained: bool = False, # IMPORTANT ): super().__init__() self.model_name = model_name if init_from_pretrained: # trust_remote_code lets us load architectures (e.g., ModernBERT/mmBERT) # that may not be bundled with the installed transformers version. self.encoder = AutoModel.from_pretrained( model_name, trust_remote_code=True, ) else: config = AutoConfig.from_pretrained( model_name, trust_remote_code=True, ) self.encoder = AutoModel.from_config( config, trust_remote_code=True, ) hidden_size = self.encoder.config.hidden_size self.dropout = nn.Dropout(dropout) self.sentiment_head = nn.Linear(hidden_size, n_sentiment) self.intent_head = nn.Linear(hidden_size, n_intent) self.topic_head = nn.Linear(hidden_size, n_topic) def forward(self, input_ids, attention_mask): outputs = self.encoder( input_ids=input_ids, attention_mask=attention_mask, ) pooled = outputs.last_hidden_state[:, 0] # CLS pooled = self.dropout(pooled) return ( self.sentiment_head(pooled), self.intent_head(pooled), self.topic_head(pooled), )

Create loader.py

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