metadata
language: zh
tags:
- roformer-v2
- pytorch
- tf2.0
介绍
tf版本
https://github.com/ZhuiyiTechnology/roformer
pytorch版本+tf2.0版本
https://github.com/JunnYu/RoFormer_pytorch
pytorch & tf2.0使用
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}
}