Examples

You can find exemplary scripts for the major down-stream tasks in examples/

Document Classification

(see examples/doc_classification.py for full script)

1.Create a tokenizer:

tokenizer = Tokenizer.load(
    pretrained_model_name_or_path=lang_model,
    do_lower_case=False)
  1. Create a DataProcessor that handles all the conversion from raw text into a pytorch Dataset:

    processor = GermEval18CoarseProcessor(tokenizer=tokenizer,
                              max_seq_len=128,
                              data_dir="../data/germeval18")
    
  2. Create a DataSilo that loads several datasets (train/dev/test), provides DataLoaders for them and calculates a few descriptive statistics of our datasets:

    data_silo = DataSilo(
        processor=processor,
        batch_size=batch_size)
    

4. Create an AdaptiveModel a) which consists of a pretrained language model as a basis:

language_model = LanguageModel.load(lang_model)
  1. and a prediction head on top that is suited for our task => Text classification:

    prediction_head = TextClassificationHead(layer_dims=[768, len(processor.label_list)])
    
    model = AdaptiveModel(
        language_model=language_model,
        prediction_heads=[prediction_head],
        embeds_dropout_prob=0.1,
        lm_output_types=["per_sequence"],
        device=device)
    
  1. Create an optimizer:

    optimizer, warmup_linear = initialize_optimizer(
        model=model,
        learning_rate=2e-5,
        warmup_proportion=0.1,
        n_examples=data_silo.n_samples("train"),
        batch_size=batch_size,
        n_epochs=1)
    
  2. 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=1,
        warmup_linear=warmup_linear,
        evaluate_every=evaluate_every,
        device=device)
    
  3. Let it grow:

    model = trainer.train(model)
    
  4. Hooray! You have a model. Store it:

    save_dir = "save/bert-german-GNAD-tutorial"
    model.save(save_dir)
    processor.save(save_dir)
    
  5. Load it & harvest your fruits (Inference):

    basic_texts = [
        {"text": "Schartau sagte dem Tagesspiegel, dass Fischer ein Idiot ist"},
        {"text": "Martin Müller spielt Fussball"},
    ]
    model = Inferencer(save_dir)
    result = model.inference_from_dicts(dicts=basic_texts)
    print(result)