DNABERT-2 论文中 DNABERT (6mer) 词汇量与 DNABERT-2 词汇量相近,其参数量 (89M) 加上这 28M,就是DNABERT-2 论文中的参数量 117M。 2.8 架构讨论 除了我们已经讨论过的 BPE Tokenization 技术,DNABERT-2 的一大改进就是换用了 ALiBi 位置编码形式,正如 ALiBi 文章标题所言,“Train Short, Test Long”,...
EPBDxDNABERT-2模型利用多模态深度学习技术,能够精确识别转录因子之间的相互作用。该模型通过整合多种类型的基因组数据,如基因表达谱、蛋白质互作网络和表观遗传修饰等,从而更全面地解析基因组的复杂性。具体来说,EPBDxDNABERT-2模型采用了先进的神经网络架构,能够处理大规模的基因组数据,并从中提取出关键的生物学特征。
Some weights of the model checkpoint at zhihan1996/DNABERT-2-117M were not used when initializing BertForSequenceClassification: ['cls.predictions.transform.LayerNorm.bias','cls.predictions.decoder.bias','cls.predictions.transform.dense.weight','cls.predictions.transform.dense.bias','cls.predictions....
别家的论文用dnabert,用dna序列训练bert模型,那么我们就用gpt训练一个gpt2模型啦,效果不管咋样,反正数据有了,下游评测也有了,换个瓶子装水就行。。。 先是数据准备,还是用人类基因组数据做训练语料,从头…
jacobfulanochanged the titleCiting MosaicBERT architecture for DNABERT_2 Pretraining WorkJan 16, 2024 jacobfulanochanged the titleCiting MosaicBERT architecture and code for DNABERT_2 Pretraining WorkJan 16, 2024 jacobfulanoclosed this ascompletedJan 16, 2024 ...
train_dataset=tokenized_dataset["train"], eval_dataset=tokenized_dataset["test"], tokenizer=tokenizer, data_collator=data_collator, compute_metrics=compute_metrics, ) trainer.train() 训练结果如下: 可以看到,二分类的精度大致能到80%左右,和dnabert等模型(83%左右)差别不是很大。
When I try "sh scripts/run_dnabert2.sh /home/DNABERT_2" I get the following error message: (base) root@f442fb5fbe89:/home/DNABERT_2/finetune# sh scripts/run_dnabert2.sh /home/DNABERT_2 The provided data_path is /home/DNABERT_2 Using the ...
If someone really wants to give it a go, this might be promising to use instead. But currently, implementation is a bit more involved than just switching out a few lines on DNABERT_2.Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment ...