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configuration_deberta.py
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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://s3.amazonaws.com/models.huggingface.co/bert/microsoft/deberta-base/config.json",
"microsoft/deberta-large": "https://s3.amazonaws.com/models.huggingface.co/bert/microsoft/deberta-large/config.json",
}
class DebertaConfig(PretrainedConfig):
r"""
:class:`~transformers.DebertaConfig` is the configuration class to store the configuration of a
:class:`~transformers.DebertaModel`.
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:`"swish"`, :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 probabilitiy 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