You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
172 lines
11 KiB
172 lines
11 KiB
# BERT Example
|
|
## Description
|
|
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).
|
|
|
|
## Requirements
|
|
- Install [MindSpore](https://www.mindspore.cn/install/en).
|
|
- Download the zhwiki dataset for pre-training. Extract and clean text in the dataset with [WikiExtractor](https://github.com/attardi/wikiextractor). Convert the dataset to TFRecord format and move the files to a specified path.
|
|
- Download dataset for fine-tuning and evaluation such as CLUENER, TNEWS, SQuAD v1.1, etc.
|
|
> Notes:
|
|
If you are running a fine-tuning or evaluation task, prepare a checkpoint from pre-train.
|
|
|
|
## Running the Example
|
|
### Pre-Training
|
|
- Set options in `config.py`, including lossscale, optimizer and network. 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_standalone_pretrain.sh` for non-distributed pre-training of BERT-base and BERT-NEZHA model.
|
|
|
|
``` bash
|
|
sh scripts/run_standalone_pretrain.sh DEVICE_ID EPOCH_SIZE DATA_DIR SCHEMA_DIR
|
|
```
|
|
- Run `run_distribute_pretrain.sh` for distributed pre-training of BERT-base and BERT-NEZHA model.
|
|
|
|
``` bash
|
|
sh scripts/run_distribute_pretrain.sh DEVICE_NUM EPOCH_SIZE DATA_DIR SCHEMA_DIR MINDSPORE_HCCL_CONFIG_PATH
|
|
```
|
|
|
|
### Fine-Tuning and Evaluation
|
|
- Set bert network config and optimizer hyperparameters in `finetune_eval_config.py`.
|
|
|
|
- Set task related hyperparameters in scripts/run_XXX.sh.
|
|
|
|
- Run `bash scripts/run_XXX.py` for fine-tuning of BERT-base and BERT-NEZHA model.
|
|
|
|
```bash
|
|
bash scripts/run_XXX.sh
|
|
```
|
|
|
|
## Usage
|
|
### Pre-Training
|
|
```
|
|
usage: run_pretrain.py [--distribute DISTRIBUTE] [--epoch_size N] [----device_num N] [--device_id N]
|
|
[--enable_save_ckpt ENABLE_SAVE_CKPT]
|
|
[--enable_lossscale ENABLE_LOSSSCALE] [--do_shuffle DO_SHUFFLE]
|
|
[--enable_data_sink ENABLE_DATA_SINK] [--data_sink_steps N] [--checkpoint_path CHECKPOINT_PATH]
|
|
[--save_checkpoint_steps N] [--save_checkpoint_num N]
|
|
[--data_dir DATA_DIR] [--schema_dir SCHEMA_DIR]
|
|
|
|
options:
|
|
--distribute pre_training by serveral devices: "true"(training by more than 1 device) | "false", default is "false"
|
|
--epoch_size epoch size: N, default is 1
|
|
--device_num number of used devices: N, default is 1
|
|
--device_id device id: N, default is 0
|
|
--enable_save_ckpt enable save checkpoint: "true" | "false", default is "true"
|
|
--enable_lossscale enable lossscale: "true" | "false", default is "true"
|
|
--do_shuffle enable shuffle: "true" | "false", default is "true"
|
|
--enable_data_sink enable data sink: "true" | "false", default is "true"
|
|
--data_sink_steps set data sink steps: N, default is 1
|
|
--checkpoint_path path to save checkpoint files: PATH, default is ""
|
|
--save_checkpoint_steps steps for saving checkpoint files: N, default is 1000
|
|
--save_checkpoint_num number for saving checkpoint files: N, default is 1
|
|
--data_dir path to dataset directory: PATH, default is ""
|
|
--schema_dir path to schema.json file, PATH, default is ""
|
|
```
|
|
## 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:
|
|
```
|
|
config.py:
|
|
bert_network version of BERT model: base | nezha, default is base
|
|
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
|
|
optimizer optimizer used in the network: AdamWerigtDecayDynamicLR | Lamb | Momentum, default is "Lamb"
|
|
|
|
scripts/run_ner.sh:
|
|
device_target targeted device to run task: Ascend | GPU
|
|
do_train whether to run training on training set: true | false
|
|
do_eval whether to run eval on dev set: true | false
|
|
assessment_method assessment method to do evaluation: f1 | clue_benchmark
|
|
use_crf whether to use crf to calculate loss: true | false
|
|
device_id device id to run task
|
|
epoch_num total number of training epochs to perform
|
|
num_class number of classes to do labeling
|
|
vocab_file_path the vocabulary file that the BERT model was trained on
|
|
label2id_file_path label to id json file
|
|
save_finetune_checkpoint_path path to save generated finetuning checkpoint
|
|
load_pretrain_checkpoint_path initial checkpoint (usually from a pre-trained BERT model)
|
|
load_finetune_checkpoint_path give a finetuning checkpoint path if only do eval
|
|
train_data_file_path ner tfrecord for training. E.g., train.tfrecord
|
|
eval_data_file_path ner tfrecord for predictions if f1 is used to evaluate result, ner json for predictions if clue_benchmark is used to evaluate result
|
|
schema_file_path path to datafile schema file
|
|
|
|
scripts/run_squad.sh:
|
|
device_target targeted device to run task: Ascend | GPU
|
|
do_train whether to run training on training set: true | false
|
|
do_eval whether to run eval on dev set: true | false
|
|
device_id device id to run task
|
|
epoch_num total number of training epochs to perform
|
|
num_class number of classes to classify, usually 2 for squad task
|
|
vocab_file_path the vocabulary file that the BERT model was trained on
|
|
eval_json_path path to squad dev json file
|
|
save_finetune_checkpoint_path path to save generated finetuning checkpoint
|
|
load_pretrain_checkpoint_path initial checkpoint (usually from a pre-trained BERT model)
|
|
load_finetune_checkpoint_path give a finetuning checkpoint path if only do eval
|
|
train_data_file_path squad tfrecord for training. E.g., train1.1.tfrecord
|
|
eval_data_file_path squad tfrecord for predictions. E.g., dev1.1.tfrecord
|
|
schema_file_path path to datafile schema file
|
|
|
|
scripts/run_classifier.sh
|
|
device_target targeted device to run task: Ascend | GPU
|
|
do_train whether to run training on training set: true | false
|
|
do_eval whether to run eval on dev set: true | false
|
|
assessment_method assessment method to do evaluation: accuracy | f1 | mcc | spearman_correlation
|
|
device_id device id to run task
|
|
epoch_num total number of training epochs to perform
|
|
num_class number of classes to do labeling
|
|
save_finetune_checkpoint_path path to save generated finetuning checkpoint
|
|
load_pretrain_checkpoint_path initial checkpoint (usually from a pre-trained BERT model)
|
|
load_finetune_checkpoint_path give a finetuning checkpoint path if only do eval
|
|
train_data_file_path tfrecord for training. E.g., train.tfrecord
|
|
eval_data_file_path tfrecord for predictions. E.g., dev.tfrecord
|
|
schema_file_path path to datafile schema file
|
|
|
|
|
|
```
|
|
|
|
### 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, must be consistant with the dataset you use. 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
|
|
learning_rate value of learning rate: Q
|
|
end_learning_rate value of end learning rate: Q, must be positive
|
|
power power: Q
|
|
warmup_steps steps of the learning rate warm up: N
|
|
weight_decay weight decay: Q
|
|
eps term added to the denominator to improve numerical stability: Q
|
|
|
|
Lamb:
|
|
decay_steps steps of the learning rate decay: N
|
|
learning_rate value of learning rate: Q
|
|
end_learning_rate value of end learning rate: Q
|
|
power power: Q
|
|
warmup_steps steps of the learning rate warm up: N
|
|
weight_decay weight decay: Q
|
|
|
|
Momentum:
|
|
learning_rate value of learning rate: Q
|
|
momentum momentum for the moving average: Q
|
|
```
|
|
|