readme: ---
  language: zh
  tags:
  - roformer
  - pytorch
  - tf2.0
  - paddlepaddle
  widget:
  - text: "今天[MASK]很好，我想去公园玩！"
  ---
  ## 介绍
  Pretrained model on 13G Chinese corpus(clue corpus small). Masked language modeling(MLM) and sentence order prediction(SOP) are used as training task.
  在13g的clue corpus small数据集上进行的预训练，使用了`Whole Mask LM` 和 `SOP` 任务
  
  训练逻辑参考了这里。https://github.com/PaddlePaddle/PaddleNLP/tree/develop/examples/language_model/ernie-1.0
  
  ## 训练细节：
  - paddlepaddle+paddlenlp
  - V100 x 4
  - batch size 256
  - max_seq_len 512 
  - max_lr 0.0001
  - min_lr 0.00001
  - weight_decay 0.01
  - grad_clip 1.0
  - 总共训练的句子```128*30w + 256*15w + 256*14.5w + 256*46.5w + 256*17w = 27648w```
  - 约等于512 batch size, 100w步条件下的54%
  
  最终loss：
  ```python
  [2022-02-05 16:05:59,067] [    INFO] - global step 170100, loss: 2.651634932, lm_loss: 2.603405, sop_loss: 0.048229, speed: 1.06 steps/s, ips: 271.68 seqs/s, learning rate: 6.66465e-05, loss_scaling: 137438.96875, num_good_steps: 356, num_bad_steps: 0
  [2022-02-05 16:07:28,227] [    INFO] - global step 170200, loss: 2.822231531, lm_loss: 2.662831, sop_loss: 0.159401, speed: 1.12 steps/s, ips: 287.13 seqs/s, learning rate: 6.66263e-05, loss_scaling: 137438.96875, num_good_steps: 59, num_bad_steps: 0
  [2022-02-05 16:08:57,346] [    INFO] - global step 170300, loss: 2.710968971, lm_loss: 2.673646, sop_loss: 0.037323, speed: 1.12 steps/s, ips: 287.26 seqs/s, learning rate: 6.66061e-05, loss_scaling: 137438.96875, num_good_steps: 159, num_bad_steps: 0
  [2022-02-05 16:10:26,698] [    INFO] - global step 170400, loss: 2.867662907, lm_loss: 2.619032, sop_loss: 0.248631, speed: 1.12 steps/s, ips: 286.51 seqs/s, learning rate: 6.65859e-05, loss_scaling: 137438.96875, num_good_steps: 259, num_bad_steps: 0
  [2022-02-05 16:11:55,714] [    INFO] - global step 170500, loss: 3.158756495, lm_loss: 2.953678, sop_loss: 0.205079, speed: 1.12 steps/s, ips: 287.59 seqs/s, learning rate: 6.65657e-05, loss_scaling: 137438.96875, num_good_steps: 359, num_bad_steps: 0
  [2022-02-05 16:13:24,869] [    INFO] - global step 170600, loss: 2.860815048, lm_loss: 2.754750, sop_loss: 0.106064, speed: 1.12 steps/s, ips: 287.14 seqs/s, learning rate: 6.65455e-05, loss_scaling: 137438.96875, num_good_steps: 33, num_bad_steps: 0
  ```
  ### tf版本 
  https://github.com/ZhuiyiTechnology/roformer
  
  ### pytorch版本+tf2.0版本
  https://github.com/JunnYu/RoFormer_pytorch
  
  ## pytorch使用
  ```python
  import torch
  from transformers import RoFormerForMaskedLM, BertTokenizer
  
  text = "今天[MASK]很好，我[MASK]去公园玩。"
  tokenizer = BertTokenizer.from_pretrained("junnyu/roformer_base_wwm_cluecorpussmall")
  pt_model = RoFormerForMaskedLM.from_pretrained("junnyu/roformer_base_wwm_cluecorpussmall")
  pt_inputs = tokenizer(text, return_tensors="pt")
  with torch.no_grad():
      pt_outputs = pt_model(**pt_inputs).logits[0]
  pt_outputs_sentence = "pytorch: "
  for i, id in enumerate(tokenizer.encode(text)):
      if id == tokenizer.mask_token_id:
          tokens = tokenizer.convert_ids_to_tokens(pt_outputs[i].topk(k=5)[1])
          pt_outputs_sentence += "[" + "||".join(tokens) + "]"
      else:
          pt_outputs_sentence += "".join(
              tokenizer.convert_ids_to_tokens([id], skip_special_tokens=True))
  print(pt_outputs_sentence)
  # pytorch: 今天[天||人||气||阳||雨]很好，我[想||就||要||也||还]去公园玩。
  
  ```
  
  ## 引用
  
  Bibtex：
  
  ```tex
  @misc{su2021roformer,
        title={RoFormer: Enhanced Transformer with Rotary Position Embedding}, 
        author={Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu},
        year={2021},
        eprint={2104.09864},
        archivePrefix={arXiv},
        primaryClass={cs.CL}
  }
  ```