def __init__(self, cfg: DictConfig, trainer: Trainer = None):
self.data_prepared = False
super().__init__(cfg=cfg, trainer=trainer)
self.encoder = SGDEncoder(
hidden_size=self.bert_model.config.hidden_size,
dropout=self._cfg.encoder.dropout)
self.decoder = SGDDecoder(
embedding_dim=self.bert_model.config.hidden_size)
self.loss = SGDDialogueStateLoss(reduction="mean")
def __init__(self, cfg: DictConfig, trainer: Trainer = None):
self.data_prepared = False
self.setup_tokenizer(cfg.tokenizer)
super().__init__(cfg=cfg, trainer=trainer)
self.bert_model = get_lm_model(
pretrained_model_name=cfg.language_model.pretrained_model_name,
config_file=cfg.language_model.config_file,
config_dict=OmegaConf.to_container(cfg.language_model.config) if cfg.language_model.config else None,
checkpoint_file=cfg.language_model.lm_checkpoint,
)
self.encoder = SGDEncoder(hidden_size=self.bert_model.config.hidden_size, dropout=self._cfg.encoder.dropout)
self.decoder = SGDDecoder(embedding_dim=self.bert_model.config.hidden_size)
self.loss = SGDDialogueStateLoss(reduction="mean")