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ColossalAI/examples/community/roberta/README.md

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# Introduction
This example introduce how to pretrain roberta from scratch, including preprocessing, pretraining, finetune. The example can help you quickly train a high-quality roberta.
## 0. Prerequisite
- Install Colossal-AI
- Editing the port from `/etc/ssh/sshd_config` and `/etc/ssh/ssh_config`, every host expose the same ssh port of server and client. If you are a root user, you also set the **PermitRootLogin** from `/etc/ssh/sshd_config` to "yes"
- Ensure that each host can log in to each other without password. If you have n hosts, need to execute n<sup>2</sup> times
```
ssh-keygen
ssh-copy-id -i ~/.ssh/id_rsa.pub ip_destination
```
- In all hosts, edit /etc/hosts to record all hosts' name and ip.The example is shown below.
```bash
192.168.2.1 GPU001
192.168.2.2 GPU002
192.168.2.3 GPU003
192.168.2.4 GPU004
192.168.2.5 GPU005
192.168.2.6 GPU006
192.168.2.7 GPU007
...
```
- restart ssh
```
service ssh restart
```
## 1. Corpus Preprocessing
```bash
cd preprocessing
```
following the `README.md`, preprocess original corpus to h5py plus numpy
## 2. Pretrain
```bash
cd pretraining
```
following the `README.md`, load the h5py generated by preprocess of step 1 to pretrain the model
## 3. Finetune
The checkpoint produced by this repo can replace `pytorch_model.bin` from [hfl/chinese-roberta-wwm-ext-large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large/tree/main) directly. Then use transformers from Hugging Face to finetune downstream application.
## Contributors
The example is contributed by AI team from [Moore Threads](https://www.mthreads.com/). If you find any problems for pretraining, please file an issue or send an email to yehua.zhang@mthreads.com. At last, welcome any form of contribution!