# Copyright 2023 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Multi-head BERT encoder network with classification heads. Includes configurations and instantiation methods. """ from typing import List, Optional, Text import dataclasses from official.modeling.hyperparams import base_config from official.nlp.configs import encoders @dataclasses.dataclass class ClsHeadConfig(base_config.Config): inner_dim: int = 0 num_classes: int = 2 activation: Optional[Text] = "tanh" dropout_rate: float = 0.0 cls_token_idx: int = 0 name: Optional[Text] = None @dataclasses.dataclass class PretrainerConfig(base_config.Config): """Pretrainer configuration.""" encoder: encoders.EncoderConfig = dataclasses.field( default_factory=encoders.EncoderConfig ) cls_heads: List[ClsHeadConfig] = dataclasses.field(default_factory=list) mlm_activation: str = "gelu" mlm_initializer_range: float = 0.02 # Currently only used for mobile bert. mlm_output_weights_use_proj: bool = False