language: zh
tags:

• roformer
• pytorch
• tf2.0
  widget:
• text: "今天[MASK]很好，我想去公园玩！"

介绍

tf版本

https://github.com/ZhuiyiTechnology/roformer

pytorch版本+tf2.0版本

https://github.com/JunnYu/RoFormer_pytorch

pytorch使用

import torch  
from transformers import RoFormerForMaskedLM, RoFormerTokenizer  

text = "今天[MASK]很好，我[MASK]去公园玩。"  
tokenizer = RoFormerTokenizer.from_pretrained("junnyu/roformer_chinese_char_small")  
pt_model = RoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_char_small")  
pt_inputs = tokenizer(text, return_tensors="pt")  
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)  
# pytorch: 今天[也||都||又||还||我]很好，我[就||想||去||也||又]去公园玩。  

tensorflow2.0使用

import tensorflow as tf  
from transformers import RoFormerTokenizer, TFRoFormerForMaskedLM  
text = "今天[MASK]很好，我[MASK]去公园玩。"  
tokenizer = RoFormerTokenizer.from_pretrained("junnyu/roformer_chinese_char_small")  
tf_model = TFRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_char_small")  
tf_inputs = tokenizer(text, return_tensors="tf")  
tf_outputs = tf_model(**tf_inputs, training=False).logits[0]  
tf_outputs_sentence = "tf2.0: "  
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)  
# tf2.0: 今天[也||都||又||还||我]很好，我[就||想||去||也||又]去公园玩。  

引用

Bibtex：

@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}  
}