blink/crossencoder/train_cross.py [131:163]:
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    return results


def get_optimizer(model, params):
    return get_bert_optimizer(
        [model],
        params["type_optimization"],
        params["learning_rate"],
        fp16=params.get("fp16"),
    )


def get_scheduler(params, optimizer, len_train_data, logger):
    batch_size = params["train_batch_size"]
    grad_acc = params["gradient_accumulation_steps"]
    epochs = params["num_train_epochs"]

    num_train_steps = int(len_train_data / batch_size / grad_acc) * epochs
    num_warmup_steps = int(num_train_steps * params["warmup_proportion"])

    scheduler = WarmupLinearSchedule(
        optimizer, warmup_steps=num_warmup_steps, t_total=num_train_steps,
    )
    logger.info(" Num optimization steps = %d" % num_train_steps)
    logger.info(" Num warmup steps = %d", num_warmup_steps)
    return scheduler


def main(params):
    model_output_path = params["output_path"]
    if not os.path.exists(model_output_path):
        os.makedirs(model_output_path)
    logger = utils.get_logger(params["output_path"])
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elq/biencoder/train_biencoder.py [167:199]:
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    return results


def get_optimizer(model, params):
    return get_bert_optimizer(
        [model],
        params["type_optimization"],
        params["learning_rate"],
        fp16=params.get("fp16"),
    )


def get_scheduler(params, optimizer, len_train_data, logger):
    batch_size = params["train_batch_size"]
    grad_acc = params["gradient_accumulation_steps"]
    epochs = params["num_train_epochs"]

    num_train_steps = int(len_train_data / batch_size / grad_acc) * epochs
    num_warmup_steps = int(num_train_steps * params["warmup_proportion"])

    scheduler = WarmupLinearSchedule(
        optimizer, warmup_steps=num_warmup_steps, t_total=num_train_steps,
    )
    logger.info(" Num optimization steps = %d" % num_train_steps)
    logger.info(" Num warmup steps = %d", num_warmup_steps)
    return scheduler


def main(params):
    model_output_path = params["output_path"]
    if not os.path.exists(model_output_path):
        os.makedirs(model_output_path)
    logger = utils.get_logger(params["output_path"])
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