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configuration_luke.py
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configuration_luke.py
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# coding=utf-8
# Copyright Studio Ousia 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.
""" LUKE configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"studio-ousia/luke-base": "https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json",
"studio-ousia/luke-large": "https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json",
}
class LukeConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`LukeModel`]. It is used to instantiate a LUKE
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 LUKE
[studio-ousia/luke-base](https://huggingface.co/studio-ousia/luke-base) architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vocab_size (`int`, *optional*, defaults to 30522):
Vocabulary size of the LUKE model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`LukeModel`].
entity_vocab_size (`int`, *optional*, defaults to 500000):
Entity vocabulary size of the LUKE model. Defines the number of different entities that can be represented
by the `entity_ids` passed when calling [`LukeModel`].
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the encoder layers and the pooler layer.
entity_emb_size (`int`, *optional*, defaults to 256):
The number of dimensions of the entity embedding.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `Callable`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention probabilities.
max_position_embeddings (`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 (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed when calling [`LukeModel`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
layer_norm_eps (`float`, *optional*, defaults to 1e-12):
The epsilon used by the layer normalization layers.
use_entity_aware_attention (`bool`, defaults to `True`):
Whether or not the model should use the entity-aware self-attention mechanism proposed in [LUKE: Deep
Contextualized Entity Representations with Entity-aware Self-attention (Yamada et
al.)](https://arxiv.org/abs/2010.01057).
classifier_dropout (`float`, *optional*):
The dropout ratio for the classification head.
Examples:
```python
>>> from transformers import LukeConfig, LukeModel
>>> # Initializing a LUKE configuration
>>> configuration = LukeConfig()
>>> # Initializing a model from the configuration
>>> model = LukeModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "luke"
def __init__(
self,
vocab_size=50267,
entity_vocab_size=500000,
hidden_size=768,
entity_emb_size=256,
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=2,
initializer_range=0.02,
layer_norm_eps=1e-12,
use_entity_aware_attention=True,
classifier_dropout=None,
pad_token_id=1,
bos_token_id=0,
eos_token_id=2,
**kwargs,
):
"""Constructs LukeConfig."""
super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
self.vocab_size = vocab_size
self.entity_vocab_size = entity_vocab_size
self.hidden_size = hidden_size
self.entity_emb_size = entity_emb_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
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.layer_norm_eps = layer_norm_eps
self.use_entity_aware_attention = use_entity_aware_attention
self.classifier_dropout = classifier_dropout