# Copyright 2022 Huawei Technologies Co., Ltd
#
# 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.
# ============================================================================
"""
Ernie config
"""
import re
from mindnlp.abc import PreTrainedConfig
from mindnlp.configs import MINDNLP_CONFIG_URL_BASE
ERNIE_SUPPORT_LIST = [
"uie-base",
"uie-medium",
"uie-mini",
"uie-micro",
"uie-nano",
"uie-base-en",
"uie-senta-base",
"uie-senta-medium",
"uie-senta-mini",
"uie-senta-micro",
"uie-senta-nano",
"uie-base-answer-extractor",
"uie-base-qa-filter",
]
CONFIG_ARCHIVE_MAP = {
model: MINDNLP_CONFIG_URL_BASE.format(re.search(r"^[^-]*", model).group(), model)
for model in ERNIE_SUPPORT_LIST
}
[docs]class ErnieConfig(PreTrainedConfig):
"""
Configuration for Ernie.
"""
pretrained_config_archive_map = CONFIG_ARCHIVE_MAP
def __init__(
self,
vocab_size: int = 30522,
hidden_size: int = 768,
num_hidden_layers: int = 12,
num_attention_heads: int = 12,
task_id=0,
intermediate_size: int = 3072,
hidden_act: str = "gelu",
hidden_dropout_prob: float = 0.1,
attention_probs_dropout_prob: float = 0.1,
max_position_embeddings: int = 512,
task_type_vocab_size: int = 3,
type_vocab_size: int = 16,
initializer_range: float = 0.02,
pad_token_id: int = 0,
pool_act: str = "tanh",
fuse: bool = False,
layer_norm_eps=1e-12,
use_cache=False,
use_task_id=True,
enable_recompute=False,
**kwargs
):
super().__init__(pad_token_id=pad_token_id, **kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.task_id = task_id
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.task_type_vocab_size = task_type_vocab_size
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.pool_act = pool_act
self.fuse = fuse
self.layer_norm_eps = layer_norm_eps
self.use_cache = use_cache
self.use_task_id = use_task_id
self.enable_recompute = enable_recompute
[docs]class UIEConfig(PreTrainedConfig):
"""
Configuration for UIE.
"""
pretrained_config_archive_map = CONFIG_ARCHIVE_MAP
def __init__(
self,
vocab_size: int = 40000,
hidden_size: int = 768,
num_hidden_layers: int = 12,
num_attention_heads: int = 12,
task_id=0,
intermediate_size: int = 3072,
hidden_act: str = "gelu",
hidden_dropout_prob: float = 0.1,
attention_probs_dropout_prob: float = 0.1,
max_position_embeddings: int = 2048,
task_type_vocab_size: int = 3,
type_vocab_size: int = 4,
initializer_range: float = 0.02,
pad_token_id: int = 0,
pool_act: str = "tanh",
fuse: bool = False,
layer_norm_eps=1e-12,
use_cache=False,
use_task_id=True,
enable_recompute=False,
**kwargs
):
super().__init__(pad_token_id=pad_token_id, **kwargs)
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.task_id = task_id
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.task_type_vocab_size = task_type_vocab_size
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
self.pool_act = pool_act
self.fuse = fuse
self.layer_norm_eps = layer_norm_eps
self.use_cache = use_cache
self.use_task_id = use_task_id
self.enable_recompute = enable_recompute
__all__ = ["ErnieConfig", "UIEConfig"]