# 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)
```
{"language": "en", "license": "mit", "tags": ["pytorch", "electra", "roformer", "rotary position embedding"], "datasets": ["openwebtext"], "thumbnail": "https://github.com/junnyu"}
transformers pytorch roformer feature-extraction electra rotary position embedding en dataset:openwebtext license:mit endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #roformer #feature-extraction #electra #rotary position embedding #en #dataset-openwebtext #license-mit #endpoints_compatible #has_space #region-us 

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)
====================



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
=====



TAGS
#transformers #pytorch #roformer #feature-extraction #electra #rotary position embedding #en #dataset-openwebtext #license-mit #endpoints_compatible #has_space #region-us 

passage: TAGS
#transformers #pytorch #roformer #feature-extraction #electra #rotary position embedding #en #dataset-openwebtext #license-mit #endpoints_compatible #has_space #region-us 
