This example implements pre-training, fine-tuning and evaluation of [BERT-base](https://github.com/google-research/bert)(the base version of BERT model) and [BERT-NEZHA](https://github.com/huawei-noah/Pretrained-Language-Model)(a Chinese pretrained language model developed by Huawei, which introduced a improvement of Functional Relative Positional Encoding as an effective positional encoding scheme).
- Download the zhwiki dataset from <https://dumps.wikimedia.org/zhwiki> for pre-training. Extract and clean text in the dataset with [WikiExtractor](https://github.com/attardi/wiliextractor). Convert the dataset to TFRecord format and move the files to a specified path.
- Download the CLUE dataset from <https://www.cluebenchmarks.com> for fine-tuning and evaluation.
> Notes:
If you are running a fine-tuning or evaluation task, prepare the corresponding checkpoint file.
## Running the Example
### Pre-Training
- Set options in `config.py`. Make sure the 'DATA_DIR'(path to the dataset) and 'SCHEMA_DIR'(path to the json schema file) are set to your own path. Click [here](https://www.mindspore.cn/tutorial/zh-CN/master/use/data_preparation/loading_the_datasets.html#tfrecord) for more information about dataset and the json schema file.
- Run `run_pretrain.py` for pre-training of BERT-base and BERT-NEZHA model.
``` bash
python run_pretrain.py --backend=ms
```
### Fine-Tuning
- Set options in `finetune_config.py`. Make sure the 'data_file', 'schema_file' and 'ckpt_file' are set to your own path, set the 'pre_training_ckpt' to save the checkpoint files generated.
- Run `finetune.py` for fine-tuning of BERT-base and BERT-NEZHA model.
```bash
python finetune.py --backend=ms
```
### Evaluation
- Set options in `evaluation_config.py`. Make sure the 'data_file', 'schema_file' and 'finetune_ckpt' are set to your own path.
- Run `evaluation.py` for evaluation of BERT-base and BERT-NEZHA model.
```bash
python evaluation.py --backend=ms
```
## Usage
### Pre-Training
```
usage: run_pretrain.py [--backend BACKEND]
optional parameters:
--backend, BACKEND MindSpore backend: ms
```
## Options and Parameters
It contains of parameters of BERT model and options for training, which is set in file `config.py`, `finetune_config.py` and `evaluation_config.py` respectively.
### Options:
```
Pre-Training:
bert_network version of BERT model: base | large, default is base
epoch_size repeat counts of training: N, default is 40
dataset_sink_mode use dataset sink mode or not: True | False, default is True
do_shuffle shuffle the dataset or not: True | False, default is True
do_train_with_lossscale use lossscale or not: True | False, default is True
loss_scale_value initial value of loss scale: N, default is 2^32
scale_factor factor used to update loss scale: N, default is 2
scale_window steps for once updatation of loss scale: N, default is 1000
save_checkpoint_steps steps to save a checkpoint: N, default is 2000
keep_checkpoint_max numbers to save checkpoint: N, default is 1
init_ckpt checkpoint file to load: PATH, default is ""
data_dir dataset file to load: PATH, default is "/your/path/cn-wiki-128"
schema_dir dataset schema file to load: PATH, default is "your/path/datasetSchema.json"
optimizer optimizer used in the network: AdamWerigtDecayDynamicLR | Lamb | Momentum, default is "Lamb"
Fine-Tuning:
task task type: NER | XNLI | LCQMC | SENTI
data_file dataset file to load: PATH, default is "/your/path/cn-wiki-128"
schema_file dataset schema file to load: PATH, default is "/your/path/datasetSchema.json"
epoch_num repeat counts of training: N, default is 40
ckpt_prefix prefix used to save checkpoint files: PREFIX, default is "bert"
ckpt_dir path to save checkpoint files: PATH, default is None
pre_training_ckpt checkpoint file to load: PATH, default is "/your/path/pre_training.ckpt"
optimizer optimizer used in the network: AdamWeigtDecayDynamicLR | Lamb | Momentum, default is "Lamb"
Evaluation:
task task type: NER | XNLI | LCQMC | SENTI
data_file dataset file to load: PATH, default is "/your/path/evaluation.tfrecord"
schema_file dataset schema file to load: PATH, default is "/your/path/schema.json"
finetune_ckpt checkpoint file to load: PATH, default is "/your/path/your.ckpt"
```
### Parameters:
```
Parameters for dataset and network (Pre-Training/Fine-Tuning/Evaluation):
batch_size batch size of input dataset: N, default is 16
seq_length length of input sequence: N, default is 128
vocab_size size of each embedding vector: N, default is 21136
hidden_size size of bert encoder layers: N, default is 768
num_hidden_layers number of hidden layers: N, default is 12
num_attention_heads number of attention heads: N, default is 12
intermediate_size size of intermediate layer: N, default is 3072
hidden_act activation function used: ACTIVATION, default is "gelu"
hidden_dropout_prob dropout probability for BertOutput: Q, default is 0.1
attention_probs_dropout_prob dropout probability for BertAttention: Q, default is 0.1
max_position_embeddings maximum length of sequences: N, default is 512
type_vocab_size size of token type vocab: N, default is 16
initializer_range initialization value of TruncatedNormal: Q, default is 0.02
use_relative_positions use relative positions or not: True | False, default is False
input_mask_from_dataset use the input mask loaded form dataset or not: True | False, default is True
token_type_ids_from_dataset use the token type ids loaded from dataset or not: True | False, default is True
dtype data type of input: mstype.float16 | mstype.float32, default is mstype.float32
compute_type compute type in BertTransformer: mstype.float16 | mstype.float32, default is mstype.float16
Parameters for optimizer:
AdamWeightDecayDynamicLR:
decay_steps steps of the learning rate decay: N, default is 12276*3
learning_rate value of learning rate: Q, default is 1e-5
end_learning_rate value of end learning rate: Q, default is 0.0
power power: Q, default is 10.0
warmup_steps steps of the learning rate warm up: N, default is 2100
weight_decay weight decay: Q, default is 1e-5
eps term added to the denominator to improve numerical stability: Q, default is 1e-6
Lamb:
decay_steps steps of the learning rate decay: N, default is 12276*3
learning_rate value of learning rate: Q, default is 1e-5
end_learning_rate value of end learning rate: Q, default is 0.0
power power: Q, default is 5.0
warmup_steps steps of the learning rate warm up: N, default is 2100
weight_decay weight decay: Q, default is 1e-5
decay_filter function to determine whether to apply weight decay on parameters: FUNCTION, default is lambda x: False
Momentum:
learning_rate value of learning rate: Q, default is 2e-5
momentum momentum for the moving average: Q, default is 0.9
