Function build_transformer_model

bert4keras/models.py:2640–2720

根据配置文件构建模型，可选加载checkpoint权重

(
    config_path=None,
    checkpoint_path=None,
    model='bert',
    application='encoder',
    return_keras_model=True,
    **kwargs
)

def build_transformer_model(
    config_path=None,
    checkpoint_path=None,
    model='bert',
    application='encoder',
    return_keras_model=True,
    **kwargs
):
    """根据配置文件构建模型，可选加载checkpoint权重
    """
    configs = {}
    if config_path is not None:
        configs.update(json.load(open(config_path)))
    configs.update(kwargs)
    if 'max_position' not in configs:
        configs['max_position'] = configs.get('max_position_embeddings', 512)
    if 'dropout_rate' not in configs:
        configs['dropout_rate'] = configs.get('hidden_dropout_prob')
    if 'attention_dropout_rate' not in configs:
        configs['attention_dropout_rate'] = configs.get(
            'attention_probs_dropout_prob'
        )
    if 'segment_vocab_size' not in configs:
        configs['segment_vocab_size'] = configs.get('type_vocab_size', 2)

    models = {
        'bert': BERT,
        'albert': ALBERT,
        'albert_unshared': ALBERT_Unshared,
        'roberta': BERT,
        'nezha': NEZHA,
        'roformer': RoFormer,
        'roformer_v2': RoFormerV2,
        'electra': ELECTRA,
        'gpt': GPT,
        'gpt2': GPT2,
        'gpt2_ml': GPT2_ML,
        't5': T5,
        't5_encoder': T5_Encoder,
        't5_decoder': T5_Decoder,
        't5.1.0': T5,
        't5.1.0_encoder': T5_Encoder,
        't5.1.0_decoder': T5_Decoder,
        't5.1.1': T5,
        't5.1.1_encoder': T5_Encoder,
        't5.1.1_decoder': T5_Decoder,
        'mt5.1.1': T5,
        'mt5.1.1_encoder': T5_Encoder,
        'mt5.1.1_decoder': T5_Decoder,
    }

    if is_string(model):
        model = model.lower()
        MODEL = models[model]
        if model.endswith('t5.1.1'):
            configs['version'] = model
    else:
        MODEL = model