我没有slurm，更改了sh文件后

!/bin/env bash

设置可见的GPU设备（例如，这里设置只使用第一块GPU）

export CUDA_VISIBLE_DEVICES=0,1

环境配置

source ~/.bashrc

设置日期时间

dt=date '+%Y%m%d_%H%M%S'

定义参数

dataset="csqa" model='roberta-large' elr="1e-5" dlr="1e-3" bs=64 mbs=2 n_epochs=30 num_relation=38 k=5 #num of gnn layers gnndim=200

输出超参数信息

echo " hyperparameters " echo "dataset: $dataset" echo "enc_name: $model" echo "batch_size: $bs" echo "learning_rate: elr $elr dlr $dlr" echo "gnn: dim $gnndim layer $k" echo "**"

设置保存模型和日志的目录

save_dir_pref='saved_models' mkdir -p $save_dir_pref mkdir -p logs

循环运行脚本

for seed in 0; do python3 -u jointlk.py --dataset $dataset \ --encoder $model -k $k --gnn_dim $gnndim -elr $elr -dlr $dlr -bs $bs -mbs $mbs --seed $seed \ --num_relation $num_relation \ --n_epochs $n_epochs --max_epochs_before_stop 10 \ --train_adj data/${dataset}/graph/train.graph.adj.pk \ --dev_adj data/${dataset}/graph/dev.graph.adj.pk \ --test_adj data/${dataset}/graph/test.graph.adj.pk \ --train_statements data/${dataset}/statement/train.statement.jsonl \ --dev_statements data/${dataset}/statement/dev.statement.jsonl \ --test_statements data/${dataset}/statement/test.statement.jsonl \ --save_model \ --save_dir ${save_dir_pref}/${dataset}/enc-${model}k${k}gnndim${gnndim}bs${bs}seed${seed}__${dt} $args \

logs/train_${dataset}enc-${model}k${k}gnndim${gnndim}bs${bs}seed${seed}${dt}.log.txt done 运行此脚本后，说是没有roberta-large权重文件，我去hugging face下载pytorch-bin后，添加--load_model_path后，显示too many values to unpack

Yueqing-Sun / JointLK

权重找不到 #4