How to use the farm.train.Trainer function in farm

prediction_heads=[lm_prediction_head],
        embeds_dropout_prob=0.1,
        lm_output_types=["per_token"],
        device=device,
    )

    model, optimizer, lr_schedule = initialize_optimizer(
        model=model,
        learning_rate=2e-5,
        #optimizer_opts={'name': 'AdamW', 'lr': 2E-05},
        n_batches=len(data_silo.loaders["train"]),
        n_epochs=1,
        device=device,
        schedule_opts={'name': 'CosineWarmup', 'warmup_proportion': 0.1}
    )
    trainer = Trainer(
        optimizer=optimizer,
        data_silo=data_silo,
        epochs=n_epochs,
        n_gpu=n_gpu,
        lr_schedule=lr_schedule,
        evaluate_every=evaluate_every,
        device=device,
    )

    model = trainer.train(model)

    # LM embeddings and weight of decoder in head are shared and should therefore be equal
    assert torch.all(
        torch.eq(model.language_model.model.embeddings.word_embeddings.weight, model.prediction_heads[0].decoder.weight))

    save_dir = "testsave/lm_finetuning_no_nsp"

prediction_heads=[lm_prediction_head, next_sentence_head],
        embeds_dropout_prob=0.1,
        lm_output_types=["per_token", "per_sequence"],
        device=device,
    )

    model, optimizer, lr_schedule = initialize_optimizer(
        model=model,
        learning_rate=2e-5,
        #optimizer_opts={'name': 'AdamW', 'lr': 2E-05},
        n_batches=len(data_silo.loaders["train"]),
        n_epochs=1,
        device=device,
        schedule_opts={'name': 'CosineWarmup', 'warmup_proportion': 0.1})

    trainer = Trainer(
        optimizer=optimizer,
        data_silo=data_silo,
        epochs=n_epochs,
        n_gpu=n_gpu,
        evaluate_every=evaluate_every,
        device=device,
    )

    model = trainer.train(model)

    # LM embeddings and weight of decoder in head are shared and should therefore be equal
    assert torch.all(
        torch.eq(model.language_model.model.embeddings.word_embeddings.weight, model.prediction_heads[0].decoder.weight))

    save_dir = "testsave/lm_finetuning"
    model.save(save_dir)

language_model=language_model,
        prediction_heads=[prediction_head],
        embeds_dropout_prob=0.1,
        lm_output_types=["per_token"],
        device=device,
    )

    model, optimizer, lr_schedule = initialize_optimizer(
        model=model,
        learning_rate=2e-5,
        #optimizer_opts={'name': 'AdamW', 'lr': 2E-05},
        n_batches=len(data_silo.loaders["train"]),
        n_epochs=n_epochs,
        device=device
    )
    trainer = Trainer(
        optimizer=optimizer,
        data_silo=data_silo,
        epochs=n_epochs,
        n_gpu=n_gpu,
        lr_schedule=lr_schedule,
        evaluate_every=evaluate_every,
        device=device
    )
    model = trainer.train(model)
    save_dir = "testsave/qa"
    model.save(save_dir)
    processor.save(save_dir)

prediction_heads=[prediction_head],
        embeds_dropout_prob=0.1,
        lm_output_types=["per_token"],
        device=device,
    )

    model, optimizer, lr_schedule = initialize_optimizer(
        model=model,
        learning_rate=2e-5,
        #optimizer_opts={'name': 'AdamW', 'lr': 2E-05},
        n_batches=len(data_silo.loaders["train"]),
        n_epochs=1,
        device=device,
        schedule_opts={'name': 'LinearWarmup', 'warmup_proportion': 0.1}
    )
    trainer = Trainer(
        optimizer=optimizer,
        data_silo=data_silo,
        epochs=n_epochs,
        n_gpu=n_gpu,
        lr_schedule=lr_schedule,
        evaluate_every=evaluate_every,
        device=device,
    )

    save_dir = "testsave/ner"
    model = trainer.train(model)
    model.save(save_dir)
    processor.save(save_dir)

    basic_texts = [
        {"text": "Albrecht Lehman ist eine Person"},

language_model=language_model,
        prediction_heads=[prediction_head],
        embeds_dropout_prob=0.1,
        lm_output_types=["per_sequence"],
        device=device)

    model, optimizer, lr_schedule = initialize_optimizer(
        model=model,
        learning_rate=2e-5,
        #optimizer_opts={'name': 'AdamW', 'lr': 2E-05},
        n_batches=len(data_silo.loaders["train"]),
        n_epochs=1,
        device=device,
        schedule_opts=None)

    trainer = Trainer(
        optimizer=optimizer,
        data_silo=data_silo,
        epochs=n_epochs,
        n_gpu=n_gpu,
        lr_schedule=lr_schedule,
        evaluate_every=evaluate_every,
        device=device)

    model = trainer.train(model)

    save_dir = "testsave/doc_class_roberta"
    model.save(save_dir)
    processor.save(save_dir)

    basic_texts = [
        {"text": "Martin Müller spielt Handball in Berlin."},

embeds_dropout_prob=0.1,
        lm_output_types=["per_sequence_continuous"],
        device=device
    )

    model, optimizer, lr_schedule = initialize_optimizer(
        model=model,
        learning_rate=2e-5,
        #optimizer_opts={'name': 'AdamW', 'lr': 2E-05},
        n_batches=len(data_silo.loaders["train"]),
        n_epochs=1,
        device=device,
        schedule_opts={'name': 'CosineWarmup', 'warmup_proportion': 0.1}
    )

    trainer = Trainer(
        optimizer=optimizer,
        data_silo=data_silo,
        epochs=n_epochs,
        n_gpu=n_gpu,
        lr_schedule=lr_schedule,
        evaluate_every=evaluate_every,
        device=device
    )

    model = trainer.train(model)

    save_dir = "testsave/doc_regr"
    model.save(save_dir)
    processor.save(save_dir)

    basic_texts = [

prediction_heads=[prediction_head],
        embeds_dropout_prob=0.1,
        lm_output_types=["per_token"],
        device=device,
    )

    # 5. Create an optimizer
    model, optimizer, lr_schedule = initialize_optimizer(
        model=model,
        learning_rate=3e-5,
        schedule_opts={"name": "LinearWarmup", "warmup_proportion": 0.2},
        n_batches=len(data_silo.loaders["train"]),
        n_epochs=n_epochs,
    )
    # 6. Feed everything to the Trainer, which keeps care of growing our model and evaluates it from time to time
    trainer = Trainer(
        optimizer=optimizer,
        data_silo=data_silo,
        epochs=n_epochs,
        n_gpu=n_gpu,
        lr_schedule=lr_schedule,
        evaluate_every=evaluate_every,
        device=device,
    )
    # 7. Let it grow! Watch the tracked metrics live on the public mlflow server: https://public-mlflow.deepset.ai
    model = trainer.train(model)

    # 8. Hooray! You have a model. Store it:
    model.save(save_dir)
    processor.save(save_dir)

embeds_dropout_prob=0.1,
    lm_output_types=["per_token"],
    device=device,
)

# 5. Create an optimizer
model, optimizer, lr_schedule = initialize_optimizer(
    model=model,
    learning_rate=1e-5,
    schedule_opts={"name": "LinearWarmup", "warmup_proportion": 0.2},
    n_batches=len(data_silo.loaders["train"]),
    n_epochs=n_epochs,
    device=device
)
# 6. Feed everything to the Trainer, which keeps care of growing our model and evaluates it from time to time
trainer = Trainer(
    optimizer=optimizer,
    data_silo=data_silo,
    epochs=n_epochs,
    n_gpu=n_gpu,
    lr_schedule=lr_schedule,
    evaluate_every=evaluate_every,
    device=device,
)
# 7. Let it grow! Watch the tracked metrics live on the public mlflow server: https://public-mlflow.deepset.ai
model = trainer.train(model)

# 8. Hooray! You have a model. Store it:
save_dir = "../saved_models/bert-english-qa-tutorial"
model.save(save_dir)
processor.save(save_dir)

model = AdaptiveModel(
    language_model=language_model,
    prediction_heads=[prediction_head],
    embeds_dropout_prob=0.1,
    lm_output_types=["per_sequence_continuous"],
    device=device)

# 5. Create an optimizer
model, optimizer, lr_schedule = initialize_optimizer(
    model=model,
    learning_rate=2e-5,
    n_batches=len(data_silo.loaders["train"]),
    n_epochs=n_epochs)

# 6. Feed everything to the Trainer, which keeps care of growing our model into powerful plant and evaluates it from time to time
trainer = Trainer(
    optimizer=optimizer,
    data_silo=data_silo,
    epochs=n_epochs,
    n_gpu=n_gpu,
    lr_schedule=lr_schedule,
    evaluate_every=evaluate_every,
    device=device)

# 7. Let it grow
model = trainer.train(model)

# 8. Hooray! You have a model. Store it:
save_dir = "saved_models/bert-doc-regression-tutorial"
model.save(save_dir)
processor.save(save_dir)

model = AdaptiveModel(
    language_model=language_model,
    prediction_heads=[prediction_head],
    embeds_dropout_prob=0.1,
    lm_output_types=["per_sequence"],
    device=device)

# 5. Create an optimizer
model, optimizer, lr_schedule = initialize_optimizer(
    model=model,
    learning_rate=3e-5,
    n_batches=len(data_silo.loaders["train"]),
    n_epochs=n_epochs)

# 6. Feed everything to the Trainer, which keeps care of growing our model into powerful plant and evaluates it from time to time
trainer = Trainer(
    optimizer=optimizer,
    data_silo=data_silo,
    epochs=n_epochs,
    n_gpu=n_gpu,
    lr_schedule=lr_schedule,
    evaluate_every=evaluate_every,
    device=device)

# 7. Let it grow
model = trainer.train(model)

# 8. Hooray! You have a model. Store it:
save_dir = "saved_models/bert-multi-doc-roberta"
model.save(save_dir)
processor.save(save_dir)