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| # coding=utf-8 | |
| # Copyright 2020, Microsoft and the HuggingFace Inc. team. | |
| # | |
| # 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. | |
| """ DeBERTa model configuration """ | |
| from ...configuration_utils import PretrainedConfig | |
| from ...utils import logging | |
| logger = logging.get_logger(__name__) | |
| DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP = { | |
| "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/config.json", | |
| "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/config.json", | |
| "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json", | |
| "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json", | |
| "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json", | |
| "microsoft/deberta-xlarge-mnli": "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json", | |
| } | |
| class DebertaConfig(PretrainedConfig): | |
| r""" | |
| This is the configuration class to store the configuration of a :class:`~transformers.DebertaModel` or a | |
| :class:`~transformers.TFDebertaModel`. It is used to instantiate a DeBERTa model according to the specified | |
| arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar | |
| configuration to that of the DeBERTa `microsoft/deberta-base <https://huggingface.co/microsoft/deberta-base>`__ | |
| architecture. | |
| Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model | |
| outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information. | |
| Arguments: | |
| vocab_size (:obj:`int`, `optional`, defaults to 30522): | |
| Vocabulary size of the DeBERTa model. Defines the number of different tokens that can be represented by the | |
| :obj:`inputs_ids` passed when calling :class:`~transformers.DebertaModel` or | |
| :class:`~transformers.TFDebertaModel`. | |
| hidden_size (:obj:`int`, `optional`, defaults to 768): | |
| Dimensionality of the encoder layers and the pooler layer. | |
| num_hidden_layers (:obj:`int`, `optional`, defaults to 12): | |
| Number of hidden layers in the Transformer encoder. | |
| num_attention_heads (:obj:`int`, `optional`, defaults to 12): | |
| Number of attention heads for each attention layer in the Transformer encoder. | |
| intermediate_size (:obj:`int`, `optional`, defaults to 3072): | |
| Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder. | |
| hidden_act (:obj:`str` or :obj:`Callable`, `optional`, defaults to :obj:`"gelu"`): | |
| The non-linear activation function (function or string) in the encoder and pooler. If string, | |
| :obj:`"gelu"`, :obj:`"relu"`, :obj:`"silu"`, :obj:`"gelu"`, :obj:`"tanh"`, :obj:`"gelu_fast"`, | |
| :obj:`"mish"`, :obj:`"linear"`, :obj:`"sigmoid"` and :obj:`"gelu_new"` are supported. | |
| hidden_dropout_prob (:obj:`float`, `optional`, defaults to 0.1): | |
| The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. | |
| attention_probs_dropout_prob (:obj:`float`, `optional`, defaults to 0.1): | |
| The dropout ratio for the attention probabilities. | |
| max_position_embeddings (:obj:`int`, `optional`, defaults to 512): | |
| The maximum sequence length that this model might ever be used with. Typically set this to something large | |
| just in case (e.g., 512 or 1024 or 2048). | |
| type_vocab_size (:obj:`int`, `optional`, defaults to 2): | |
| The vocabulary size of the :obj:`token_type_ids` passed when calling :class:`~transformers.DebertaModel` or | |
| :class:`~transformers.TFDebertaModel`. | |
| initializer_range (:obj:`float`, `optional`, defaults to 0.02): | |
| The standard deviation of the truncated_normal_initializer for initializing all weight matrices. | |
| layer_norm_eps (:obj:`float`, `optional`, defaults to 1e-12): | |
| The epsilon used by the layer normalization layers. | |
| relative_attention (:obj:`bool`, `optional`, defaults to :obj:`False`): | |
| Whether use relative position encoding. | |
| max_relative_positions (:obj:`int`, `optional`, defaults to 1): | |
| The range of relative positions :obj:`[-max_position_embeddings, max_position_embeddings]`. Use the same | |
| value as :obj:`max_position_embeddings`. | |
| pad_token_id (:obj:`int`, `optional`, defaults to 0): | |
| The value used to pad input_ids. | |
| position_biased_input (:obj:`bool`, `optional`, defaults to :obj:`True`): | |
| Whether add absolute position embedding to content embedding. | |
| pos_att_type (:obj:`List[str]`, `optional`): | |
| The type of relative position attention, it can be a combination of :obj:`["p2c", "c2p", "p2p"]`, e.g. | |
| :obj:`["p2c"]`, :obj:`["p2c", "c2p"]`, :obj:`["p2c", "c2p", 'p2p"]`. | |
| layer_norm_eps (:obj:`float`, optional, defaults to 1e-12): | |
| The epsilon used by the layer normalization layers. | |
| """ | |
| model_type = "deberta" | |
| def __init__( | |
| self, | |
| vocab_size=50265, | |
| hidden_size=768, | |
| num_hidden_layers=12, | |
| num_attention_heads=12, | |
| intermediate_size=3072, | |
| hidden_act="gelu", | |
| hidden_dropout_prob=0.1, | |
| attention_probs_dropout_prob=0.1, | |
| max_position_embeddings=512, | |
| type_vocab_size=0, | |
| initializer_range=0.02, | |
| layer_norm_eps=1e-7, | |
| relative_attention=False, | |
| max_relative_positions=-1, | |
| pad_token_id=0, | |
| position_biased_input=True, | |
| pos_att_type=None, | |
| pooler_dropout=0, | |
| pooler_hidden_act="gelu", | |
| **kwargs | |
| ): | |
| super().__init__(**kwargs) | |
| self.hidden_size = hidden_size | |
| self.num_hidden_layers = num_hidden_layers | |
| self.num_attention_heads = num_attention_heads | |
| self.intermediate_size = intermediate_size | |
| self.hidden_act = hidden_act | |
| self.hidden_dropout_prob = hidden_dropout_prob | |
| self.attention_probs_dropout_prob = attention_probs_dropout_prob | |
| self.max_position_embeddings = max_position_embeddings | |
| self.type_vocab_size = type_vocab_size | |
| self.initializer_range = initializer_range | |
| self.relative_attention = relative_attention | |
| self.max_relative_positions = max_relative_positions | |
| self.pad_token_id = pad_token_id | |
| self.position_biased_input = position_biased_input | |
| # Backwards compatibility | |
| if type(pos_att_type) == str: | |
| pos_att_type = [x.strip() for x in pos_att_type.lower().split("|")] | |
| self.pos_att_type = pos_att_type | |
| self.vocab_size = vocab_size | |
| self.layer_norm_eps = layer_norm_eps | |
| self.pooler_hidden_size = kwargs.get("pooler_hidden_size", hidden_size) | |
| self.pooler_dropout = pooler_dropout | |
| self.pooler_hidden_act = pooler_hidden_act | |