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---
language: zh
tags:
- roformer
- pytorch
- tf2.0
widget:
- text: "今天[MASK]很好,我想去公园玩!"
---
# 下载这个roformer代码
- https://github.com/JunnYu/RoFormer_pytorch/files/8402725/roformer.zip
# 使用
```python
import torch
import numpy as np
from roformer import RoFormerForCausalLM, RoFormerConfig
from transformers import BertTokenizer
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
pretrained_model = "junnyu/roformer_chinese_sim_char_base"
tokenizer = RoFormerTokenizer.from_pretrained(pretrained_model)
config = RoFormerConfig.from_pretrained(pretrained_model)
config.is_decoder = True
config.eos_token_id = tokenizer.sep_token_id
config.pooler_activation = "linear"
model = RoFormerForCausalLM.from_pretrained(pretrained_model, config=config)
model.to(device)
model.eval()
def gen_synonyms(text, n=100, k=20):
''''含义: 产生sent的n个相似句,然后返回最相似的k个。
做法:用seq2seq生成,并用encoder算相似度并排序。
'''
# 寻找所有相似的句子
r = []
inputs1 = tokenizer(text, return_tensors="pt")
for _ in range(n):
inputs1.to(device)
output = tokenizer.batch_decode(model.generate(**inputs1, top_p=0.95, do_sample=True, max_length=128), skip_special_tokens=True)[0].replace(" ","").replace(text, "") # 去除空格,去除原始text文本。
r.append(output)
# 对相似的句子进行排序
r = [i for i in set(r) if i != text and len(i) > 0]
r = [text] + r
inputs2 = tokenizer(r, padding=True, return_tensors="pt")
with torch.no_grad():
inputs2.to(device)
outputs = model(**inputs2)
Z = outputs.pooler_output.cpu().numpy()
Z /= (Z**2).sum(axis=1, keepdims=True)**0.5
argsort = np.dot(Z[1:], -Z[0]).argsort()
return [r[i + 1] for i in argsort[:k]]
out = gen_synonyms("广州和深圳哪个好?")
print(out)
# ['深圳和广州哪个好?',
# '广州和深圳哪个好',
# '深圳和广州哪个好',
# '深圳和广州哪个比较好。',
# '深圳和广州哪个最好?',
# '深圳和广州哪个比较好',
# '广州和深圳那个比较好',
# '深圳和广州哪个更好?',
# '深圳与广州哪个好',
# '深圳和广州,哪个比较好',
# '广州与深圳比较哪个好',
# '深圳和广州哪里比较好',
# '深圳还是广州比较好?',
# '广州和深圳哪个地方好一些?',
# '广州好还是深圳好?',
# '广州好还是深圳好呢?',
# '广州与深圳哪个地方好点?',
# '深圳好还是广州好',
# '广州好还是深圳好',
# '广州和深圳哪个城市好?']
```