readme: ---
  language: en
  thumbnail: https://github.com/junnyu
  tags:
  - pytorch
  - electra
  - roformer
  - rotary position embedding
  license: mit
  datasets:
  - openwebtext
  ---
  # 一、 个人在openwebtext数据集上添加rotary-position-embedding，训练得到的electra-small模型
  
  # 二、 复现结果(dev dataset)
  |Model|CoLA|SST|MRPC|STS|QQP|MNLI|QNLI|RTE|Avg.|
  |---|---|---|---|---|---|---|---|---|---|
  |ELECTRA-Small-OWT(original)|56.8|88.3|87.4|86.8|88.3|78.9|87.9|68.5|80.36|
  |**ELECTRA-RoFormer-Small-OWT (this)**|55.76|90.45|87.3|86.64|89.61|81.17|88.85|62.71|80.31|
  
  # 三、 训练细节
  - 数据集 openwebtext
  - 训练batch_size 256
  - 学习率lr  5e-4
  - 最大句子长度max_seqlen  128
  - 训练total step  50W
  - GPU RTX3090
  - 训练时间总共耗费55h
  
  # 四、wandb日志 
  - [**预训练日志**](https://wandb.ai/junyu/electra_rotary_small_pretrain?workspace=user-junyu)
  - [**GLUE微调日志**](https://wandb.ai/junyu/electra_rotary_glue_100?workspace=user-junyu)
  
  
  # 五、 使用
  ```python
  import torch
  from transformers import ElectraTokenizer,RoFormerModel
  tokenizer = ElectraTokenizer.from_pretrained("junnyu/roformer_small_discriminator")
  model = RoFormerModel.from_pretrained("junnyu/roformer_small_discriminator")
  inputs = tokenizer("Beijing is the capital of China.", return_tensors="pt")
  with torch.no_grad():
      outputs = model(**inputs)
      print(outputs[0].shape)
  ```