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| # 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 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 | |||||
| ``` | |||||