def _build_args(args): args["model"] = _parse_model_string(args["model"]) args["loss"] = _parse_loss_string(args["loss"]) a = fasttext.args() for (k, v) in args.items(): setattr(a, k, v) a.output = "" # User should use save_model a.saveOutput = 0 # Never use this if a.wordNgrams <= 1 and a.maxn == 0: a.bucket = 0 return a
def _build_args(args): args["model"] = _parse_model_string(args["model"]) args["loss"] = _parse_loss_string(args["loss"]) a = fasttext.args() for (k, v) in args.items(): setattr(a, k, v) a.test = "" # Unused a.output = "" # User should use save_model a.pretrainedVectors = "" # Unsupported a.saveOutput = 0 # Never use this return a
def _build_args(args, manually_set_args): args["model"] = _parse_model_string(args["model"]) args["loss"] = _parse_loss_string(args["loss"]) if type(args["autotuneModelSize"]) == int: args["autotuneModelSize"] = str(args["autotuneModelSize"]) a = fasttext.args() for (k, v) in args.items(): setattr(a, k, v) if k in manually_set_args: a.setManual(k) a.output = "" # User should use save_model a.saveOutput = 0 # Never use this if a.wordNgrams <= 1 and a.maxn == 0: a.bucket = 0 return a