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1.3 更好的Embedding方式:预训练模型ELMo、GPT以及BERT 使用W2V可以有效地在向量空间描述文本,但同时也存在一个问题:同一个单词的不同语义特征都被Embedding到了同一个vector内,即一词多义。比如在一个句子中bank可以指代银行,也可以指代河岸,但都是用同一个向量[1.2,1.8,4.2]来描述,使用vector时无法理解到底是哪...
莫烦喜欢化繁为简。内容覆盖BERT, GPT, Transformer, ELMo, Seq2Seq, Attention, W2V。 更多莫烦Python NLP教学:https://mofanpy.com/tutorials/machine-learning/nlp 教学代码:https://github.com/MorvanZhou/NLP-Tutorials 莫烦分享的原因:https://mofanpy.com/support 展开更多...
首先,核心结构上,BERT并没有颠覆性的创新。 其实, ELMO+ Transformer = BERT结构 GPT + Bi-Directional LM = BERT 结构 其次,fine-tuning方面,类似GPT,需要改造下游网络结构,输入输出,以适应大部分NLP任务。 【see bert google research github for detail】 ...
莫烦喜欢化繁为简。内容覆盖BERT, GPT, Transformer, ELMo, Seq2Seq, Attention, W2V。 更多莫烦Python NLP教学:https://mofanpy.com/tutorials/machine-learning/nlp 教学代码:https://github.com/MorvanZhou/NLP-Tutorials 莫烦分享的原因:https://mofanpy.com/support 展开更多...
* Update fine-tune-w2v2-bert.md * Update fine-tune-w2v2-bert.md Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com> --- Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>main ylacombe...
* Update fine-tune-w2v2-bert.md * Update fine-tune-w2v2-bert.md Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com> --- Co-authored-by: Sanchit Gandhi <93869735+sanchit-gandhi@users.noreply.github.com>main (huggingface...
莫烦喜欢化繁为简。内容覆盖BERT, GPT, Transformer, ELMo, Seq2Seq, Attention, W2V。 更多莫烦Python NLP教学:https://mofanpy.com/tutorials/machine-learning/nlp 教学代码:https://github.com/MorvanZhou/NLP-Tutorials 莫烦分享的原因:https://mofanpy.com/support 展开更多...
以 ELMo 为代表的动态词向量模型开启了语言型预训练的大门,此后以 GPT 和 BERT 为代表的基于 Transformer 模的大规模预训练语言模型的出现,使得自然语言处理全面进入了预训练微调范式新时代。 4)第四阶段:大规模预训练语言模型(Large Language Model,LLM)。2020 年Open AI 发布了由包含 1750 亿参数的神经网络构成...