The BERT network was proposed by Google in 2018. The network has made a breakthrough in the field of NLP. The network uses pre-training and fine-tune to achieve a large network structure without modifying the large network structure, and only by adding an output layer to achieve multiple text types tasks. The backbone code of BERT adopts the Encoder structure of Transformer. The attention mechanism is introduced to enable the output layer to capture high-latitude global semantic information. The pre-training uses denosing and self-encoding tasks, namely MLM(Masked Language Model) and NSP(Next Sentence Prediction). No need to label data, pre-training can be performed on massive text data, and a small amount of fine-tune can be used for downstream tasks to obtain better results. The pre-training plus fune-tune mode created by BERT is widely adopted by subsequent NLP networks.
The BERT network was proposed by Google in 2018. The network has made a breakthrough in the field of NLP. The network uses pre-training to achieve a large network structure without modifying, and only by adding an output layer to achieve multiple text-based tasks in fine-tuning. The backbone code of BERT adopts the Encoder structure of Transformer. The attention mechanism is introduced to enable the output layer to capture high-latitude global semantic information. The pre-training uses denoising and self-encoding tasks, namely MLM(Masked Language Model) and NSP(Next Sentence Prediction). No need to label data, pre-training can be performed on massive text data, and only a small amount of data to fine-tuning downstream tasks to obtain good results. The pre-training plus fune-tuning mode created by BERT is widely adopted by subsequent NLP networks.
[Paper](https://arxiv.org/abs/1810.04805): Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova. [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding]((https://arxiv.org/abs/1810.04805)). arXiv preprint arXiv:1810.04805.
[Paper](https://arxiv.org/abs/1909.00204): Junqiu Wei, Xiaozhe Ren, Xiaoguang Li, Wenyong Huang, Yi Liao, Yasheng Wang, Jiashu Lin, Xin Jiang, Xiao Chen, Qun Liu. [NEZHA: Neural Contextualized Representation for Chinese Language Understanding](https://arxiv.org/abs/1909.00204). arXiv preprint arXiv:1909.00204.
# [Model Architecture](#contents)
The backbone structure of BERT is transformer. For BERT_base, the transformer contains 12 encoder modules, one encoder contains one selfattention module and one selfattention module contains one attention module; For BERT_NEZHA, the transformer contains 24 encoder modules, one encoder contains one selfattention module and one selfattention module contains one attention module. The difference between BERT_base and BERT_NEZHA is that BERT_base uses absolute position encoding to produce position embedding vector and BERT_NEZHA uses relative position encoding.
The backbone structure of BERT is transformer. For BERT_base, the transformer contains 12 encoder modules, each module contains one self-attention module and each self-attention module contains one attention module. For BERT_NEZHA, the transformer contains 24 encoder modules, each module contains one self-attention module and each self-attention module contains one attention module. The difference between BERT_base and BERT_NEZHA is that BERT_base uses absolute position encoding to produce position embedding vector and BERT_NEZHA uses relative position encoding.
# [Dataset](#contents)
- Download the zhwiki or enwiki dataset for pre-training. Extract and refine texts in the dataset with [WikiExtractor](https://github.com/attardi/wikiextractor). Convert the dataset to TFRecord format. Please refer to create_pretraining_data.py file in [BERT](https://github.com/google-research/bert) repository.
- Download dataset for fine-tuning and evaluation such as CLUENER, TNEWS, SQuAD v1.1, etc. Convert dataset files from json format to tfrecord format, please refer to run_classifier.py which in [BERT](https://github.com/google-research/bert) repository.
- Download dataset for fine-tuning and evaluation such as CLUENER, TNEWS, SQuAD v1.1, etc. Convert dataset files from JSON format to TFRECORD format, please refer to run_classifier.py file in [BERT](https://github.com/google-research/bert) repository.
The command above will run in the background, you can view the results the file pretraining_log.txt. After training, you will get some checkpoint files under the script folder by default. The loss value will be achieved as follows:
The command above will run in the background, you can view training logs in pretraining_log.txt. After training finished, you will get some checkpoint files under the script folder by default. The loss values will be displayed as follows:
```
# grep "epoch" pretraining_log.txt
epoch: 0.0, current epoch percent: 0.000, step: 1, outpus are (Tensor(shape=[1], dtype=Float32, [ 1.0856101e+01]), Tensor(shape=[], dtype=Bool, False), Tensor(shape=[], dtype=Float32, 65536))
...
...
@@ -366,11 +366,11 @@ epoch: 0.0, current epoch percent: 0.000, step: 2, outpus are (Tensor(shape=[1],
The command above will run in the background, you can view the results the file pretraining_log.txt. After training, you will get some checkpoint files under the LOG* folder by default. The loss value will be achieved as follows:
The command above will run in the background, you can view training logs in pretraining_log.txt. After training finished, you will get some checkpoint files under the LOG* folder by default. The loss value will be displayed as follows:
```
# grep "epoch" LOG*/pretraining_log.txt
epoch: 0.0, current epoch percent: 0.001, step: 100, outpus are (Tensor(shape=[1], dtype=Float32, [ 1.08209e+01]), Tensor(shape=[], dtype=Bool, False), Tensor(shape=[], dtype=Float32, 65536))
...
...
@@ -388,7 +388,7 @@ Before running the command below, please check the load pretrain checkpoint path
```
bash scripts/run_classifier.sh
The command above will run in the background, you can view the results the file classfier_log.txt.
The command above will run in the background, you can view training logs in classfier_log.txt.
If you choose accuracy as assessment method, the result will be as follows:
