Python HFBackboneConfig示例

示例#1

def test_model_has_correct_cfg():
    model = TranslationTransformer(
        HFBackboneConfig(
            pretrained_model_name_or_path="patrickvonplaten/t5-tiny-random"))
    assert model.hparams.downstream_model_type == "transformers.AutoModelForSeq2SeqLM"
    assert type(model.cfg) is TranslationConfig

示例#2

def test_model_has_correct_cfg():
    model = MaskedLanguageModelingTransformer(
        HFBackboneConfig(pretrained_model_name_or_path='prajjwal1/bert-tiny'))
    assert model.hparams.downstream_model_type == 'transformers.AutoModelForMaskedLM'

示例#3

文件： test_text_classification.py 项目： taeminlee/lightning-transformers

def test_model_has_correct_cfg():
    model = TextClassificationTransformer(
        HFBackboneConfig(pretrained_model_name_or_path='bert-base-cased'))
    assert model.hparams.downstream_model_type == 'transformers.AutoModelForSequenceClassification'

示例#4

文件： test_question_answering.py 项目： taeminlee/lightning-transformers

def test_model_has_correct_cfg():
    model = QuestionAnsweringTransformer(
        HFBackboneConfig(pretrained_model_name_or_path='bert-base-cased'))
    assert model.hparams.downstream_model_type == 'transformers.AutoModelForQuestionAnswering'

示例#5

文件： test_summarization.py 项目： taeminlee/lightning-transformers

def test_model_has_correct_cfg():
    model = SummarizationTransformer(
        HFBackboneConfig(pretrained_model_name_or_path='t5-base'))
    assert model.hparams.downstream_model_type == 'transformers.AutoModelForSeq2SeqLM'
    assert type(model.cfg) is SummarizationConfig

示例#6

def test_model_has_correct_cfg():
    model = LanguageModelingTransformer(
        HFBackboneConfig(pretrained_model_name_or_path="sshleifer/tiny-gpt2"))
    assert model.hparams.downstream_model_type == "transformers.AutoModelForCausalLM"