forked from mindspore-Ecosystem/mindspore
120 lines
3.0 KiB
Python
120 lines
3.0 KiB
Python
# Copyright 2020 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.
|
|
# ============================================================================
|
|
|
|
"""
|
|
config settings, will be used in finetune.py
|
|
"""
|
|
|
|
from easydict import EasyDict as edict
|
|
import mindspore.common.dtype as mstype
|
|
from mindspore.model_zoo.Bert_NEZHA import BertConfig
|
|
|
|
cfg = edict({
|
|
'task': 'NER',
|
|
'num_labels': 41,
|
|
'data_file': '/your/path/train.tfrecord',
|
|
'schema_file': '/your/path/schema.json',
|
|
'epoch_num': 5,
|
|
'ckpt_prefix': 'bert',
|
|
'ckpt_dir': None,
|
|
'pre_training_ckpt': '/your/path/pre_training.ckpt',
|
|
'use_crf': False,
|
|
'optimizer': 'Lamb',
|
|
'AdamWeightDecayDynamicLR': edict({
|
|
'learning_rate': 2e-5,
|
|
'end_learning_rate': 1e-7,
|
|
'power': 1.0,
|
|
'weight_decay': 1e-5,
|
|
'eps': 1e-6,
|
|
}),
|
|
'Lamb': edict({
|
|
'start_learning_rate': 2e-5,
|
|
'end_learning_rate': 1e-7,
|
|
'power': 1.0,
|
|
'decay_filter': lambda x: False,
|
|
}),
|
|
'Momentum': edict({
|
|
'learning_rate': 2e-5,
|
|
'momentum': 0.9,
|
|
}),
|
|
})
|
|
|
|
bert_net_cfg = BertConfig(
|
|
batch_size=16,
|
|
seq_length=128,
|
|
vocab_size=21128,
|
|
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=2,
|
|
initializer_range=0.02,
|
|
use_relative_positions=False,
|
|
input_mask_from_dataset=True,
|
|
token_type_ids_from_dataset=True,
|
|
dtype=mstype.float32,
|
|
compute_type=mstype.float16,
|
|
)
|
|
|
|
tag_to_index = {
|
|
"O": 0,
|
|
"S_address": 1,
|
|
"B_address": 2,
|
|
"M_address": 3,
|
|
"E_address": 4,
|
|
"S_book": 5,
|
|
"B_book": 6,
|
|
"M_book": 7,
|
|
"E_book": 8,
|
|
"S_company": 9,
|
|
"B_company": 10,
|
|
"M_company": 11,
|
|
"E_company": 12,
|
|
"S_game": 13,
|
|
"B_game": 14,
|
|
"M_game": 15,
|
|
"E_game": 16,
|
|
"S_government": 17,
|
|
"B_government": 18,
|
|
"M_government": 19,
|
|
"E_government": 20,
|
|
"S_movie": 21,
|
|
"B_movie": 22,
|
|
"M_movie": 23,
|
|
"E_movie": 24,
|
|
"S_name": 25,
|
|
"B_name": 26,
|
|
"M_name": 27,
|
|
"E_name": 28,
|
|
"S_organization": 29,
|
|
"B_organization": 30,
|
|
"M_organization": 31,
|
|
"E_organization": 32,
|
|
"S_position": 33,
|
|
"B_position": 34,
|
|
"M_position": 35,
|
|
"E_position": 36,
|
|
"S_scene": 37,
|
|
"B_scene": 38,
|
|
"M_scene": 39,
|
|
"E_scene": 40,
|
|
"<START>": 41,
|
|
"<STOP>": 42
|
|
}
|