junnyu/roformer_small_discriminator

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
• GLUE微调日志

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)