基于Transformers,albert_pytorch,用来简化生成句向量的代码,方便后续进行文本相似度计算等。
见requirements.txt
直接运行flask_api.py,用cosine距离判断句子的相似度,可以看到相似句子的bert和albert直接输出的向量相余弦距离比较小。
句向量输出代码主要参考了Bert提取句子特征(pytorch_transformers)。
一条命令产生bert、albert句向量,用于相似度计算和文本分类等。
基于Transformers,albert_pytorch,用来简化生成句向量的代码,方便后续进行文本相似度计算等。
见requirements.txt
直接运行flask_api.py,用cosine距离判断句子的相似度,可以看到相似句子的bert和albert直接输出的向量相余弦距离比较小。
句向量输出代码主要参考了Bert提取句子特征(pytorch_transformers)。
我的场景是两个文本的相似度,思路是:微调后,抽取的最后一层池化作为了句向量,用余弦来计算相似度。是768维的,效果还可以,后来因为有个需求需要降低到128维,我直接在768维后面接了一个全连接降到128维了。但是效果就不好了....是不是这种方式不可行?
你好,我从提示位置下载了Albert的预训练模型,并尝试直接运行文件/albert/albert_total.py,得到报错 AttributeError: 'BertConfig' object has no attribute 'ln_type'。看起来问题是是缺少In_type参数。我查看了albert模型的config文件也没有这个In_type参数。请问如何解决呢?
使用的都是官方链接下载的pytorch版本的bert和albert模型(各3个文件)
transformer版本是跟要求吻合的2.4.1
运行报错:
"Unable to load weights from pytorch checkpoint file. "
OSError: Unable to load weights from pytorch checkpoint file. If you tried to load a PyTorch model from a TF 2.0 checkpoint, please set from_tf=True.
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