def _get_shared_models(self, args: "DictConfig") -> Dict[str, dict]: _ = args # Not needed models_needed = list(self.conversations_needed.keys()) active_models = [ m for m in models_needed if self.conversations_needed[m] > 0 ] return TurkLikeAgent.get_bot_agents(args=args, active_models=active_models)
def _get_shared_models(self, args: "DictConfig") -> Dict[str, dict]: with open(args.blueprint.model_opt_path) as f: all_model_opts = yaml.safe_load(f.read()) active_model_opts = { model: opt for model, opt in all_model_opts.items() if self.conversations_needed[model] > 0 } return TurkLikeAgent.get_bot_agents(args=args, model_opts=active_model_opts)
def get_bot_worker(opt: Dict[str, Any], model_name: str) -> TurkLikeAgent: """ Return a bot agent. Agent behaves like a crowdsource worker but actually wraps around a dialogue model. """ semaphore = opt['semaphore'] shared_bot_agents = opt['shared_bot_agents'] num_turns = opt['num_turns'] bot_agent = create_agent_from_shared(shared_bot_agents[model_name]) bot_worker = TurkLikeAgent( opt, model_name=model_name, model_agent=bot_agent, num_turns=num_turns, semaphore=semaphore, ) return bot_worker
def _get_shared_models(self, args: "DictConfig") -> Dict[str, dict]: with open(args.blueprint.model_opt_path) as f: model_opts = yaml.safe_load(f.read()) return TurkLikeAgent.get_bot_agents(args=args, model_opts=model_opts)