blink/biencoder/train_biencoder.py [193:224]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    )

    number_of_samples_per_dataset = {}

    time_start = time.time()

    utils.write_to_file(
        os.path.join(model_output_path, "training_params.txt"), str(params)
    )

    logger.info("Starting training")
    logger.info(
        "device: {} n_gpu: {}, distributed training: {}".format(device, n_gpu, False)
    )

    optimizer = get_optimizer(model, params)
    scheduler = get_scheduler(params, optimizer, len(train_tensor_data), logger)

    model.train()

    best_epoch_idx = -1
    best_score = -1

    num_train_epochs = params["num_train_epochs"]
    for epoch_idx in trange(int(num_train_epochs), desc="Epoch"):
        tr_loss = 0
        results = None

        if params["silent"]:
            iter_ = train_dataloader
        else:
            iter_ = tqdm(train_dataloader, desc="Batch")
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blink/crossencoder/train_cross.py [261:293]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    )

    number_of_samples_per_dataset = {}

    time_start = time.time()

    utils.write_to_file(
        os.path.join(model_output_path, "training_params.txt"), str(params)
    )

    logger.info("Starting training")
    logger.info(
        "device: {} n_gpu: {}, distributed training: {}".format(device, n_gpu, False)
    )

    optimizer = get_optimizer(model, params)
    scheduler = get_scheduler(params, optimizer, len(train_tensor_data), logger)

    model.train()

    best_epoch_idx = -1
    best_score = -1

    num_train_epochs = params["num_train_epochs"]

    for epoch_idx in trange(int(num_train_epochs), desc="Epoch"):
        tr_loss = 0
        results = None

        if params["silent"]:
            iter_ = train_dataloader
        else:
            iter_ = tqdm(train_dataloader, desc="Batch")
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