mindspore/example/bert_clue/finetune_config.py

# 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
}