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)
