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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

五、 使用

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)
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Dataset used to train junnyu/roformer_small_discriminator