"CoLA":'https://dl.fbaipublicfiles.com/glue/data/CoLA.zip',
"SST":'https://dl.fbaipublicfiles.com/glue/data/SST-2.zip',
"QQP":'https://dl.fbaipublicfiles.com/glue/data/QQP-clean.zip',
"STS":'https://dl.fbaipublicfiles.com/glue/data/STS-B.zip',
"MNLI":'https://dl.fbaipublicfiles.com/glue/data/MNLI.zip',
"QNLI":'https://dl.fbaipublicfiles.com/glue/data/QNLIv2.zip',
"RTE":'https://dl.fbaipublicfiles.com/glue/data/RTE.zip',
"WNLI":'https://dl.fbaipublicfiles.com/glue/data/WNLI.zip'
MRPC_TRAIN = "https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt"
MRPC_TEST = "https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_test.txt"
采用的是bert-base-uncased模型,需要将下述四个文件下载放在一个文件夹内
https://huggingface.co/bert-base-uncased/resolve/main/config.json
https://huggingface.co/bert-base-uncased/resolve/main/pytorch_model.bin
https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json
https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt
将config文件中的预训练模型和数据路径配置好,执行train.py
rank配置方式为,组之间用-分隔,每个组为层数*秩的样式,层数为1可以省略1*,秩为0代表该层不分解,类似于4*32-4*1-3*32-64这种形式,baseline设置为12*0即可
pytorch==1.10.2
transformers==4.20.1
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