How to use the farm.infer.Inferencer function in farm

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)
    processor.save(save_dir)

    basic_texts = [
        {"text": "Farmer's life is great."},
        {"text": "It's nothing for big city kids though."},
    ]
    model = Inferencer.load(save_dir, embedder_only=True)
    result = model.extract_vectors(dicts=basic_texts)
    assert result[0]["context"] == ['Farmer', "'", 's', 'life', 'is', 'great', '.']
    assert result[0]["vec"].shape == (768,)
    # TODO check why results vary accross runs with same seed
    assert isinstance(result[0]["vec"][0], np.float32)

evaluate_every=evaluate_every,
        device=device)

    model = trainer.train(model)

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

    basic_texts = [
        {"text": "Martin Müller spielt Handball in Berlin."},
        {"text": "Schartau sagte dem Tagesspiegel, dass Fischer ein Idiot sei."}
    ]


    inf = Inferencer.load(save_dir, batch_size=2)
    result = inf.inference_from_dicts(dicts=basic_texts)
    assert isinstance(result[0]["predictions"][0]["probability"], np.float32)

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"},
    ]
    model = Inferencer.load(save_dir)
    result = model.inference_from_dicts(dicts=basic_texts, max_processes=1)
    print(result)
    #assert result[0]["predictions"][0]["context"] == "sagte"

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)


if inference:
    model = Inferencer.load(save_dir, batch_size=32, gpu=True)
    full_result = model.inference_from_file(
        file=inference_file,
        max_processes=max_processes_for_inference,
    )

    for x in full_result:
        print(x)
        print()

    result = {r["id"]: r["preds"][0][0] for r in full_result}
    full_result =  {r["id"]: r["preds"] for r in full_result}

    json.dump(result,
              open(predictions_file, "w"),
              indent=4,
              ensure_ascii=False)

# defined with the EarlyStopping instance
# The model we have at this moment is the model from the last training epoch that was carried
# out before early stopping terminated the training
save_dir = "saved_models/bert-german-doc-tutorial"
model.save(save_dir)
processor.save(save_dir)

# 9. Load it & harvest your fruits (Inference)
basic_texts = [
    {"text": "Schartau sagte dem Tagesspiegel, dass Fischer ein Idiot sei"},
    {"text": "Martin Müller spielt Handball in Berlin"},
]

# Load from the final epoch directory and apply
print("LOADING INFERENCER FROM FINAL MODEL DURING TRAINING")
model = Inferencer.load(save_dir)
result = model.inference_from_dicts(dicts=basic_texts)
print(result)

# Load from saved best model
print("LOADING INFERENCER FROM BEST MODEL DURING TRAINING")
model = Inferencer.load(earlystopping.save_dir)
result = model.inference_from_dicts(dicts=basic_texts)
print("APPLICATION ON BEST MODEL")
print(result)

device=device,)

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

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

# 9. Load it & harvest your fruits (Inference)
basic_texts = [
    {"text": "Schartau sagte dem Tagesspiegel, dass Fischer ein Idiot sei"},
    {"text": "Martin Müller spielt Handball in Berlin"},
]
model = Inferencer.load(save_dir)
result = model.inference_from_dicts(dicts=basic_texts)
print(result)

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)

# 9. Load it & harvest your fruits (Inference)
basic_texts = [
    {"text": "You fucking bastards"},
    {"text": "What a lovely world"},
]
model = Inferencer.load(save_dir)
result = model.run_inference(dicts=basic_texts)
print(result)

# 9. Load it & harvest your fruits (Inference)
basic_texts = [
    {"text": "Schartau sagte dem Tagesspiegel, dass Fischer ein Idiot sei"},
    {"text": "Martin Müller spielt Handball in Berlin"},
]

# Load from the final epoch directory and apply
print("LOADING INFERENCER FROM FINAL MODEL DURING TRAINING")
model = Inferencer.load(save_dir)
result = model.inference_from_dicts(dicts=basic_texts)
print(result)

# Load from saved best model
print("LOADING INFERENCER FROM BEST MODEL DURING TRAINING")
model = Inferencer.load(earlystopping.save_dir)
result = model.inference_from_dicts(dicts=basic_texts)
print("APPLICATION ON BEST MODEL")
print(result)

# 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)

# 9. Load it & harvest your fruits (Inference)
#    Add your own text adapted to the dataset you provide
basic_texts = [
    {"text": ""},
    {"text": ""},
]
model = Inferencer.load(save_dir)
result = model.inference_from_dicts(dicts=basic_texts)

print(result)

# 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)

# 9. Load it & harvest your fruits (Inference)
QA_input = [
        {
            "questions": ["Who counted the game among the best ever made?"],
            "text":  "Twilight Princess was released to universal critical acclaim and commercial success. It received perfect scores from major publications such as 1UP.com, Computer and Video Games, Electronic Gaming Monthly, Game Informer, GamesRadar, and GameSpy. On the review aggregators GameRankings and Metacritic, Twilight Princess has average scores of 95% and 95 for the Wii version and scores of 95% and 96 for the GameCube version. GameTrailers in their review called it one of the greatest games ever created."
        }]

model = Inferencer.load(save_dir, batch_size=40, gpu=True)
result = model.inference_from_dicts(dicts=QA_input)

for x in result:
    pprint.pprint(x)

# 10. Do Inference on whole SQuAD Dataset & write the predictions file to disk
filename = os.path.join(processor.data_dir,processor.dev_filename)
result = model.inference_from_file(file=filename)

write_squad_predictions(
    predictions=result,
    predictions_filename=filename,
    out_filename="predictions.json"
)