---
language: zh
tags:
- roformer-v2
- pytorch
- tf2.0
inference: False
---
## 介绍
### tf版本 
https://github.com/ZhuiyiTechnology/roformer-v2

### pytorch版本+tf2.0版本
https://github.com/JunnYu/RoFormer_pytorch

### 安装
- pip install roformer==0.4.3

## 评测对比

### CLUE榜单分类任务结果，base版本。

|         | iflytek | tnews | afqmc | cmnli | ocnli | wsc | csl |
| :-----: | :-----: | :---: | :---: | :---: | :---: | :---: | :---: |
| BERT | 60.06 | 56.80 | 72.41 | 79.56 | 73.93 | 78.62 | 83.93 |
| RoBERTa | 60.64 | 58.06 | 74.05 | 81.24 | 76.00 | **87.50** | 84.50 |
| RoFormer | 60.91 | 57.54 | 73.52 | 80.92 | **76.07** | 86.84 | 84.63 |
| GAU-α | 61.41 | 57.76 | 74.17** | **81.82** | 75.86 | 79.93 | **85.67** |
| RoFormerV2<sup>*</sup> | 60.87 | 56.54 | 72.75 | 80.34 | 75.36 | 80.92 | 84.67 |
| RoFormerV2<sup>*</sup>-pytorch(本仓库代码) | **63.15** | **58.24** | **75.42** | 80.59 | 74.17 |   83.79   | 83.73 |

## pytorch & tf2.0使用
```python
import torch
import tensorflow as tf
from transformers import BertTokenizer
from roformer import RoFormerForMaskedLM, TFRoFormerForMaskedLM

text = "今天[MASK]很好，我[MASK]去公园玩。"
tokenizer = BertTokenizer.from_pretrained("junnyu/roformer_v2_chinese_char_base")
pt_model = RoFormerForMaskedLM.from_pretrained("junnyu/roformer_v2_chinese_char_base")
tf_model = TFRoFormerForMaskedLM.from_pretrained(
    "junnyu/roformer_v2_chinese_char_base", from_pt=True
)
pt_inputs = tokenizer(text, return_tensors="pt")
tf_inputs = tokenizer(text, return_tensors="tf")
# pytorch
with torch.no_grad():
    pt_outputs = pt_model(**pt_inputs).logits[0]
pt_outputs_sentence = "pytorch: "
for i, id in enumerate(tokenizer.encode(text)):
    if id == tokenizer.mask_token_id:
        tokens = tokenizer.convert_ids_to_tokens(pt_outputs[i].topk(k=5)[1])
        pt_outputs_sentence += "[" + "||".join(tokens) + "]"
    else:
        pt_outputs_sentence += "".join(
            tokenizer.convert_ids_to_tokens([id], skip_special_tokens=True)
        )
print(pt_outputs_sentence)
# tf
tf_outputs = tf_model(**tf_inputs, training=False).logits[0]
tf_outputs_sentence = "tf: "
for i, id in enumerate(tokenizer.encode(text)):
    if id == tokenizer.mask_token_id:
        tokens = tokenizer.convert_ids_to_tokens(tf.math.top_k(tf_outputs[i], k=5)[1])
        tf_outputs_sentence += "[" + "||".join(tokens) + "]"
    else:
        tf_outputs_sentence += "".join(
            tokenizer.convert_ids_to_tokens([id], skip_special_tokens=True)
        )
print(tf_outputs_sentence)
# small
# pytorch: 今天[的||，||是||很||也]很好，我[要||会||是||想||在]去公园玩。
# tf: 今天[的||，||是||很||也]很好，我[要||会||是||想||在]去公园玩。
# base
# pytorch: 今天[我||天||晴||园||玩]很好，我[想||要||会||就||带]去公园玩。
# tf: 今天[我||天||晴||园||玩]很好，我[想||要||会||就||带]去公园玩。
# large
# pytorch: 今天[天||气||我||空||阳]很好，我[又||想||会||就||爱]去公园玩。
# tf: 今天[天||气||我||空||阳]很好，我[又||想||会||就||爱]去公园玩。
```

## 引用
Bibtex：
```tex
@misc{su2021roformer,
      title={RoFormer: Enhanced Transformer with Rotary Position Embedding}, 
      author={Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu},
      year={2021},
      eprint={2104.09864},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
```
```tex
@techreport{roformerv2,
  title={RoFormerV2: A Faster and Better RoFormer - ZhuiyiAI},
  author={Jianlin Su, Shengfeng Pan, Bo Wen, Yunfeng Liu},
  year={2022},
  url="https://github.com/ZhuiyiTechnology/roformer-v2",
}
```