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)